Thiol/disulfide redox states in signaling and sensing

PubMed Central

Go, Young-Mi; Jones, Dean P.

2015-01-01

Rapid advances in redox systems biology are creating new opportunities to understand complexities of human disease and contributions of environmental exposures. New understanding of thiol-disulfide systems have occurred during the past decade as a consequence of the discoveries that thiol and disulfide systems are maintained in kinetically controlled steady-states displaced from thermodynamic equilibrium, that a widely distributed family of NADPH oxidases produces oxidants that function in cell signaling, and that a family of peroxiredoxins utilize thioredoxin as a reductant to complement the well-studied glutathione antioxidant system for peroxide elimination and redox regulation. This review focuses on thiol/disulfide redox state in biologic systems and the knowledge base available to support development of integrated redox systems biology models to better understand the function and dysfunction of thiol-disulfide redox systems. In particular, central principles have emerged concerning redox compartmentalization and utility of thiol/disulfide redox measures as indicators of physiologic function. Advances in redox proteomics show that, in addition to functioning in protein active sites and cell signaling, cysteine residues also serve as redox sensors to integrate biologic functions. These advances provide a framework for translation of redox systems biology concepts to practical use in understanding and treating human disease. Biological responses to cadmium, a widespread environmental agent, are used to illustrate the utility of these advances to the understanding of complex pleiotropic toxicities. PMID:23356510

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

PubMed

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

2011-07-20

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

Exploring the Complexity of Tree Thinking Expertise in an Undergraduate Systematics Course

ERIC Educational Resources Information Center

Halverson, Kristy L.; Pires, Chris J.; Abell, Sandra K.

2011-01-01

Student understanding of biological representations has not been well studied. Yet, we know that to be efficient problem solvers in evolutionary biology and systematics, college students must develop expertise in thinking with a particular type of representation, phylogenetic trees. The purpose of this study was to understand how undergraduates…

Plant Systems Biology at the Single-Cell Level.

PubMed

Libault, Marc; Pingault, Lise; Zogli, Prince; Schiefelbein, John

2017-11-01

Our understanding of plant biology is increasingly being built upon studies using 'omics and system biology approaches performed at the level of the entire plant, organ, or tissue. Although these approaches open new avenues to better understand plant biology, they suffer from the cellular complexity of the analyzed sample. Recent methodological advances now allow plant scientists to overcome this limitation and enable biological analyses of single-cells or single-cell-types. Coupled with the development of bioinformatics and functional genomics resources, these studies provide opportunities for high-resolution systems analyses of plant phenomena. In this review, we describe the recent advances, current challenges, and future directions in exploring the biology of single-cells and single-cell-types to enhance our understanding of plant biology as a system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of systems approaches in the study of rheumatic diseases.

PubMed

Kim, Ki-Jo; Lee, Saseong; Kim, Wan-Uk

2015-03-01

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

Using multi-criteria analysis of simulation models to understand complex biological systems

Treesearch

Maureen C. Kennedy; E. David Ford

2011-01-01

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multi-criteria optimization with Pareto optimality allows for model outputs to be compared to multiple system...

Decoding the Heart through Next Generation Sequencing Approaches.

PubMed

Pawlak, Michal; Niescierowicz, Katarzyna; Winata, Cecilia Lanny

2018-06-07

: Vertebrate organs develop through a complex process which involves interaction between multiple signaling pathways at the molecular, cell, and tissue levels. Heart development is an example of such complex process which, when disrupted, results in congenital heart disease (CHD). This complexity necessitates a holistic approach which allows the visualization of genome-wide interaction networks, as opposed to assessment of limited subsets of factors. Genomics offers a powerful solution to address the problem of biological complexity by enabling the observation of molecular processes at a genome-wide scale. The emergence of next generation sequencing (NGS) technology has facilitated the expansion of genomics, increasing its output capacity and applicability in various biological disciplines. The application of NGS in various aspects of heart biology has resulted in new discoveries, generating novel insights into this field of study. Here we review the contributions of NGS technology into the understanding of heart development and its disruption reflected in CHD and discuss how emerging NGS based methodologies can contribute to the further understanding of heart repair.

Tagging and Purifying Proteins to Teach Molecular Biology and Advanced Biochemistry

ERIC Educational Resources Information Center

Roecklein-Canfield, Jennifer A.; Lopilato, Jane

2004-01-01

Two distinct courses, "Molecular Biology" taught by the Biology Department and "Advanced Biochemistry" taught by the Chemistry Department, complement each other and, when taught in a coordinated and integrated way, can enhance student learning and understanding of complex material. "Molecular Biology" is a comprehensive lecture-based course with a…

Search for organising principles: understanding in systems biology.

PubMed

Mesarovic, M D; Sreenath, S N; Keene, J D

2004-06-01

Due in large measure to the explosive progress in molecular biology, biology has become arguably the most exciting scientific field. The first half of the 21st century is sometimes referred to as the 'era of biology', analogous to the first half of the 20th century, which was considered to be the 'era of physics'. Yet, biology is facing a crisis--or is it an opportunity--reminiscent of the state of biology in pre-double-helix time. The principal challenge facing systems biology is complexity. According to Hood, 'Systems biology defines and analyses the interrelationships of all of the elements in a functioning system in order to understand how the system works.' With 30000+ genes in the human genome the study of all relationships simultaneously becomes a formidably complex problem. Hanahan and Weinberg raised the question as to whether progress will consist of 'adding further layers of complexity to a scientific literature that is already complex almost beyond measure' or whether the progress will lead to a 'science with a conceptual structure and logical coherence that rivals that of chemistry or physics.' At the core of the challenge is the need for a new approach, a shift from reductionism to a holistic perspective. However, more than just a pronouncement of a new approach is needed. We suggest that what is needed is to provide a conceptual framework for systems biology research. We propose that the concept of a complex system, i.e. a system of systems as defined in mathematical general systems theory (MGST), is central to provide such a framework. We further argue that for a deeper understanding in systems biology investigations should go beyond building numerical mathematical or computer models--important as they are. Biological phenomena cannot be predicted with the level of numerical precision as in classical physics. Explanations in terms of how the categories of systems are organised to function in ever changing conditions are more revealing. Non-numerical mathematical tools are appropriate for the task. Such a categorical perspective led us to propose that the core of understanding in systems biology depends on the search for organising principles rather than solely on construction of predictive descriptions (i.e. models) that exactly outline the evolution of systems in space and time. The search for organising principles requires an identification/discovery of new concepts and hypotheses. Some of them, such as coordination motifs for transcriptional regulatory networks and bounded autonomy of levccels in a hierarchy, are outlined in this article. Experimental designs are outlined to help verify the applicability of the interaction balance principle of coordination to transcriptional and posttranscriptional networks.

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

Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

PubMed Central

Tyler, Anna L.; Asselbergs, Folkert W.; Williams, Scott M.; Moore, Jason H.

2011-01-01

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene-gene interaction, has also been treated as an exception to the Mendelian one gene-one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena. PMID:19204994

Pattern dynamics of the reaction-diffusion immune system.

PubMed

Zheng, Qianqian; Shen, Jianwei; Wang, Zhijie

2018-01-01

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

Improving Students' Understanding and Perception of Cell Theory in School Biology Using a Computer-Based Instruction Simulation Program

ERIC Educational Resources Information Center

Kiboss, Joel; Wekesa, Eric; Ndirangu, Mwangi

2006-01-01

A survey by the Kenya National Examination Council (KNEC) revealed that students' academic performance and interest in secondary school biology has been generally poor. This has been attributed to the current methods of instruction and the lack of instructional resources amenable to the study and proper understanding of such complex areas as cell…

From biological and social network metaphors to coupled bio-social wireless networks

PubMed Central

Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

2010-01-01

Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

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

PubMed Central

2011-01-01

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

Simulating complex intracellular processes using object-oriented computational modelling.

PubMed

Johnson, Colin G; Goldman, Jacki P; Gullick, William J

2004-11-01

The aim of this paper is to give an overview of computer modelling and simulation in cellular biology, in particular as applied to complex biochemical processes within the cell. This is illustrated by the use of the techniques of object-oriented modelling, where the computer is used to construct abstractions of objects in the domain being modelled, and these objects then interact within the computer to simulate the system and allow emergent properties to be observed. The paper also discusses the role of computer simulation in understanding complexity in biological systems, and the kinds of information which can be obtained about biology via simulation.

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.

Drosophila melanogaster--the model organism of choice for the complex biology of multi-cellular organisms

NASA Technical Reports Server (NTRS)

Beckingham, Kathleen M.; Armstrong, J. Douglas; Texada, Michael J.; Munjaal, Ravi; Baker, Dean A.

2005-01-01

Drosophila melanogaster has been intensely studied for almost 100 years. The sophisticated array of genetic and molecular tools that have evolved for analysis of gene function in this organism are unique. Further, Drosophila is a complex multi-cellular organism in which many aspects of development and behavior parallel those in human beings. These combined advantages have permitted research in Drosophila to make seminal contributions to the understanding of fundamental biological processes and ensure that Drosophila will continue to provide unique insights in the genomic era. An overview of the genetic methodologies available in Drosophila is given here, together with examples of outstanding recent contributions of Drosophila to our understanding of cell and organismal biology. The growing contribution of Drosophila to our knowledge of gravity-related responses is addressed.

Development and Effectiveness of an Educational Card Game as Supplementary Material in Understanding Selected Topics in Biology

ERIC Educational Resources Information Center

Gutierrez, Arnel F.

2014-01-01

The complex concepts and vocabulary of biology classes discourage many students. In this study, a pretest-posttest model was used to test the effectiveness of an educational card game in reinforcing biological concepts in comparison with traditional teaching methods. The subjects of this study were two biology classes at Bulacan State…

Analyzing Change in Students' Gene-to-Evolution Models in College-Level Introductory Biology

ERIC Educational Resources Information Center

Dauer, Joseph T.; Momsen, Jennifer L.; Speth, Elena Bray; Makohon-Moore, Sasha C.; Long, Tammy M.

2013-01-01

Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational…

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

PubMed Central

2011-01-01

Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga. PMID:22035155

Are attractors 'strange', or is life more complicated than the simple laws of physics?

PubMed

Pogun, S

2001-01-01

Interesting and intriguing questions involve complex systems whose properties cannot be explained fully by reductionist approaches. Last century was dominated by physics, and applying the simple laws of physics to biology appeared to be a practical solution to understand living organisms. However, although some attributes of living organisms involve physico-chemical properties, the genetic program and evolutionary history of complex biological systems make them unique and unpredictable. Furthermore, there are and will be 'unobservable' phenomena in biology which have to be accounted for.

Systems interface biology

PubMed Central

Doyle, Francis J; Stelling, Jörg

2006-01-01

The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines. PMID:16971329

A taxonomy of visualization tasks for the analysis of biological pathway data.

PubMed

Murray, Paul; McGee, Fintan; Forbes, Angus G

2017-02-15

Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Characterising the development of the understanding of human body systems in high-school biology students - a longitudinal study

NASA Astrophysics Data System (ADS)

Snapir, Zohar; Eberbach, Catherine; Ben-Zvi-Assaraf, Orit; Hmelo-Silver, Cindy; Tripto, Jaklin

2017-10-01

Science education today has become increasingly focused on research into complex natural, social and technological systems. In this study, we examined the development of high-school biology students' systems understanding of the human body, in a three-year longitudinal study. The development of the students' system understanding was evaluated using the Components Mechanisms Phenomena (CMP) framework for conceptual representation. We coded and analysed the repertory grid personal constructs of 67 high-school biology students at 4 points throughout the study. Our data analysis builds on the assumption that systems understanding entails a perception of all the system categories, including structures within the system (its Components), specific processes and interactions at the macro and micro levels (Mechanisms), and the Phenomena that present the macro scale of processes and patterns within a system. Our findings suggest that as the learning process progressed, the systems understanding of our students became more advanced, moving forward within each of the major CMP categories. Moreover, there was an increase in the mechanism complexity presented by the students, manifested by more students describing mechanisms at the molecular level. Thus, the 'mechanism' category and the micro level are critical components that enable students to understand system-level phenomena such as homeostasis.

My Dog's Cheeks: A PBL Project on Collagen for Cell Biology and Genetics Courses

ERIC Educational Resources Information Center

Casla, Alberto Vicario; Zubiaga, Isabel Smith

2010-01-01

Students often have an oversimplified view of biological facts, which may hinder subsequent understanding when conceptual complexity gives rise to cognitive conflicts. To avoid this situation here, we present a PBL approach for the analysis of Ehlers-Danlos syndrome (EDS), which integrates a variety of topics in cell biology, genetics, and…

Interpreting narratives of motivation and schizophrenia: a biopsychosocial understanding.

PubMed

Boydell, Katherine M; Gladstone, Brenda M; Volpe, Tiziana

2003-01-01

The concept of motivation involves a complex interplay of biopsychosocial and environmental determinants. For individuals diagnosed with schizophrenia, motivation has traditionally been approached from a neuro-biological standpoint, obscuring this complexity. The findings from this study underscore the importance of broadening our understanding of motivation and schizophrenia through an exploration of individual perspectives and identification of the psychosocial factors that clarify the experience of diminished motivation.

The genotype-phenotype map of an evolving digital organism.

PubMed

Fortuna, Miguel A; Zaman, Luis; Ofria, Charles; Wagner, Andreas

2017-02-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

The genotype-phenotype map of an evolving digital organism

PubMed Central

Zaman, Luis; Wagner, Andreas

2017-01-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable. PMID:28241039

Mathematics for understanding disease.

PubMed

Bies, R R; Gastonguay, M R; Schwartz, S L

2008-06-01

The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.

NOVEL MARKERS OF AIR POLLUTION-INDUCED VASCULAR TOXICITY

EPA Science Inventory

The results of this project should be a handful of biological markers that can be subsequently used to: 1) identify susceptible individuals, 2) identify causal components of the complex air pollution mixture, and 3) better understand the biological mechanisms involved in air p...

Ask not what physics can do for biology--ask what biology can do for physics.

PubMed

Frauenfelder, Hans

2014-10-08

Stan Ulam, the famous mathematician, said once to Hans Frauenfelder: 'Ask not what Physics can do for biology, ask what biology can do for physics'. The interaction between biologists and physicists is a two-way street. Biology reveals the secrets of complex systems, physics provides the physical tools and the theoretical concepts to understand the complexity. The perspective gives a personal view of the path to some of the physical concepts that are relevant for biology and physics (Frauenfelder et al 1999 Rev. Mod. Phys. 71 S419-S442). Schrödinger's book (Schrödinger 1944 What is Life? (Cambridge: Cambridge University Press)), loved by physicists and hated by eminent biologists (Dronamraju 1999 Genetics 153 1071-6), still shows how a great physicist looked at biology well before the first protein structure was known.

New insights into pancreatic cancer biology.

PubMed

Hidalgo, M

2012-09-01

Pancreatic cancer remains a devastating disease. Over the last few years, there have been important advances in the molecular and biological understanding of pancreatic cancer. This included understanding of the genomic complexity of the disease, the role of pancreatic cancer stem cells, the relevance of the tumor microenvironment, and the unique metabolic adaptation of pancreas cancer cells to obtain nutrients under hypoxic environment. In this paper, we review the most salient developments in these few areas.

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

NASA Astrophysics Data System (ADS)

Goswami, Monojoy; Sumpter, Bobby

2014-03-01

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

Industrial systems biology and its impact on synthetic biology of yeast cell factories.

PubMed

Fletcher, Eugene; Krivoruchko, Anastasia; Nielsen, Jens

2016-06-01

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal of developing improved yeast cell factories. Biotechnol. Bioeng. 2016;113: 1164-1170. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

First-Year Biology Students' Understandings of Meiosis: An Investigation Using a Structural Theoretical Framework

ERIC Educational Resources Information Center

Quinn, Frances; Pegg, John; Panizzon, Debra

2009-01-01

Meiosis is a biological concept that is both complex and important for students to learn. This study aims to explore first-year biology students' explanations of the process of meiosis, using an explicit theoretical framework provided by the Structure of the Observed Learning Outcome (SOLO) model. The research was based on responses of 334…

Evolutionary cell biology: functional insight from "endless forms most beautiful".

PubMed

Richardson, Elisabeth; Zerr, Kelly; Tsaousis, Anastasios; Dorrell, Richard G; Dacks, Joel B

2015-12-15

In animal and fungal model organisms, the complexities of cell biology have been analyzed in exquisite detail and much is known about how these organisms function at the cellular level. However, the model organisms cell biologists generally use include only a tiny fraction of the true diversity of eukaryotic cellular forms. The divergent cellular processes observed in these more distant lineages are still largely unknown in the general scientific community. Despite the relative obscurity of these organisms, comparative studies of them across eukaryotic diversity have had profound implications for our understanding of fundamental cell biology in all species and have revealed the evolution and origins of previously observed cellular processes. In this Perspective, we will discuss the complexity of cell biology found across the eukaryotic tree, and three specific examples of where studies of divergent cell biology have altered our understanding of key functional aspects of mitochondria, plastids, and membrane trafficking. © 2015 Richardson et al. 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).

COMPLEX MIXTURES OF CHEMICAL CARCINOGENS: PRINCIPLES OF ACTION AND HUMAN CANCER

EPA Science Inventory

There is strong epidemiological evidence supported by experimental animal data that complex environmental mixtures pose a risk to human health producing increases in cancer incidence. Understanding the chemical and biological properties of these mixtures leads to a clearer unde...

Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection.

PubMed

Rehman, Zia Ur; Idris, Adnan; Khan, Asifullah

2018-06-01

Protein-Protein Interactions (PPI) play a vital role in cellular processes and are formed because of thousands of interactions among proteins. Advancements in proteomics technologies have resulted in huge PPI datasets that need to be systematically analyzed. Protein complexes are the locally dense regions in PPI networks, which extend important role in metabolic pathways and gene regulation. In this work, a novel two-phase protein complex detection and grouping mechanism is proposed. In the first phase, topological and biological features are extracted for each complex, and prediction performance is investigated using Bagging based Ensemble classifier (PCD-BEns). Performance evaluation through cross validation shows improvement in comparison to CDIP, MCode, CFinder and PLSMC methods Second phase employs Multi-Dimensional Scaling (MDS) for the grouping of known complexes by exploring inter complex relations. It is experimentally observed that the combination of topological and biological features in the proposed approach has greatly enhanced prediction performance for protein complex detection, which may help to understand various biological processes, whereas application of MDS based exploration may assist in grouping potentially similar complexes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interactions of platinum metals and their complexes in biological systems.

PubMed Central

LeRoy, A F

1975-01-01

Platinum-metal oxidation catalysts are to be introduced in exhaust systems of many 1975 model-year automobiles in the U.S. to meet Clean Air Act standards. Small quantities of finely divided catalyst have been found issuing from prototype systems; platinum and palladium compounds may be found also. Although platinum exhibits a remarkable resistance to oxidation and chemical attack, it reacts chemically under some conditions producing coordination complex compounds. Palladium reacts more readily than platinum. Some platinum-metal complexes interact with biological systems as bacteriostatic, bacteriocidal, viricidal, and immunosuppressive agents. Workers chronically exposed to platinum complexes often develop asthma-like respiratory distress and skin reactions called platinosis. Platinum complexes used alone and in combination therapy with other drugs have recently emerged as effective agents in cancer chemotherapy. Understanding toxic and favorable interactions of metal species with living organisms requires basic information on quantities and chemical characteristics of complexes at trace concentrations in biological materials. Some basic chemical kinetic and thermodynamic data are presented to characterize the chemical behavior of the complex cis-[Pt(NH3)2Cl2] used therapeutically. A brief discussion of platinum at manogram levels in biological tissue is discussed. PMID:50943

Unraveling the Pathogenesis of Hoyeraal-Hreidarsson Syndrome, a Complex Telomere Biology Disorder

PubMed Central

Glousker, Galina; Touzot, Fabien; Revy, Patrick; Tzfati, Yehuda; Savage, Sharon A.

2015-01-01

SUMMARY Hoyeraal-Hreidarsson (HH) syndrome is a multisystem genetic disorder characterized by very short telomeres and considered a clinically severe variant of dyskeratosis congenita (DC). The main cause of mortality, usually in early childhood, is bone marrow failure. Mutations in several telomere biology genes have been reported to cause HH in about 60% of the HH patients, but the genetic defects in the rest of the patients are still unknown. Understanding the aetiology of HH and its diverse manifestations is challenging because of the complexity of telomere biology and the multiple telomeric and non-telomeric functions played by telomere-associated proteins in processes such as telomere replication, telomere protection, DNA damage response and ribosome and spliceosome assembly. Here we review the known clinical complications, molecular defects and germline mutations associated with HH, and elucidate possible mechanistic explanations and remaining questions in our understanding of the disease. PMID:25940403

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology.

PubMed

Aretz, Ina; Meierhofer, David

2016-04-27

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

PubMed Central

Aretz, Ina; Meierhofer, David

2016-01-01

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology. PMID:27128910

Systems Biology-Based Platforms to Accelerate Research of Emerging Infectious Diseases.

PubMed

Oh, Soo Jin; Choi, Young Ki; Shin, Ok Sarah

2018-03-01

Emerging infectious diseases (EIDs) pose a major threat to public health and security. Given the dynamic nature and significant impact of EIDs, the most effective way to prevent and protect against them is to develop vaccines in advance. Systems biology approaches provide an integrative way to understand the complex immune response to pathogens. They can lead to a greater understanding of EID pathogenesis and facilitate the evaluation of newly developed vaccine-induced immunity in a timely manner. In recent years, advances in high throughput technologies have enabled researchers to successfully apply systems biology methods to analyze immune responses to a variety of pathogens and vaccines. Despite recent advances, computational and biological challenges impede wider application of systems biology approaches. This review highlights recent advances in the fields of systems immunology and vaccinology, and presents ways that systems biology-based platforms can be applied to accelerate a deeper understanding of the molecular mechanisms of immunity against EIDs. © Copyright: Yonsei University College of Medicine 2018.

Systems Biology-Based Platforms to Accelerate Research of Emerging Infectious Diseases

PubMed Central

2018-01-01

Emerging infectious diseases (EIDs) pose a major threat to public health and security. Given the dynamic nature and significant impact of EIDs, the most effective way to prevent and protect against them is to develop vaccines in advance. Systems biology approaches provide an integrative way to understand the complex immune response to pathogens. They can lead to a greater understanding of EID pathogenesis and facilitate the evaluation of newly developed vaccine-induced immunity in a timely manner. In recent years, advances in high throughput technologies have enabled researchers to successfully apply systems biology methods to analyze immune responses to a variety of pathogens and vaccines. Despite recent advances, computational and biological challenges impede wider application of systems biology approaches. This review highlights recent advances in the fields of systems immunology and vaccinology, and presents ways that systems biology-based platforms can be applied to accelerate a deeper understanding of the molecular mechanisms of immunity against EIDs. PMID:29436184

Nanomaterial interactions with biomembranes: Bridging the gap between soft matter models and biological context.

PubMed

Werner, Marco; Auth, Thorsten; Beales, Paul A; Fleury, Jean Baptiste; Höök, Fredrik; Kress, Holger; Van Lehn, Reid C; Müller, Marcus; Petrov, Eugene P; Sarkisov, Lev; Sommer, Jens-Uwe; Baulin, Vladimir A

2018-04-03

Synthetic polymers, nanoparticles, and carbon-based materials have great potential in applications including drug delivery, gene transfection, in vitro and in vivo imaging, and the alteration of biological function. Nature and humans use different design strategies to create nanomaterials: biological objects have emerged from billions of years of evolution and from adaptation to their environment resulting in high levels of structural complexity; in contrast, synthetic nanomaterials result from minimalistic but controlled design options limited by the authors' current understanding of the biological world. This conceptual mismatch makes it challenging to create synthetic nanomaterials that possess desired functions in biological media. In many biologically relevant applications, nanomaterials must enter the cell interior to perform their functions. An essential transport barrier is the cell-protecting plasma membrane and hence the understanding of its interaction with nanomaterials is a fundamental task in biotechnology. The authors present open questions in the field of nanomaterial interactions with biological membranes, including: how physical mechanisms and molecular forces acting at the nanoscale restrict or inspire design options; which levels of complexity to include next in computational and experimental models to describe how nanomaterials cross barriers via passive or active processes; and how the biological media and protein corona interfere with nanomaterial functionality. In this Perspective, the authors address these questions with the aim of offering guidelines for the development of next-generation nanomaterials that function in biological media.

Fractal Branching in Vascular Trees and Networks by VESsel GENeration Analysis (VESGEN)

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, Patricia A.

2016-01-01

Vascular patterning offers an informative multi-scale, fractal readout of regulatory signaling by complex molecular pathways. Understanding such molecular crosstalk is important for physiological, pathological and therapeutic research in Space Biology and Astronaut countermeasures. When mapped out and quantified by NASA's innovative VESsel GENeration Analysis (VESGEN) software, remodeling vascular patterns become useful biomarkers that advance out understanding of the response of biology and human health to challenges such as microgravity and radiation in space environments.

Deconstructing sexual orientation: understanding the phenomena of sexual orientation.

PubMed

Stein, T S

1997-01-01

The very terms of a debate about whether or not sexual orientation is primarily a biological phenomenon fail to consider the complex origins of the phenomenon. Deconstruction of the term "homosexuality" shows that it refers to multiple factors which cannot be studied as or subsumed under a unitary concept. Adequate understanding of sexual orientation must consider the developmental, interpersonal, experiential, and cultural dimensions of sexuality, as well as any biological contributions to sexual attraction, behavior, and identity.

Noninvasive imaging of protein-protein interactions in living organisms.

PubMed

Haberkorn, Uwe; Altmann, Annette

2003-06-01

Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.

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

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.

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

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

NASA Technical Reports Server (NTRS)

Williams Colin P.

1999-01-01

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

Structural Glycomic Analyses at High Sensitivity: A Decade of Progress

NASA Astrophysics Data System (ADS)

Alley, William R.; Novotny, Milos V.

2013-06-01

The field of glycomics has recently advanced in response to the urgent need for structural characterization and quantification of complex carbohydrates in biologically and medically important applications. The recent success of analytical glycobiology at high sensitivity reflects numerous advances in biomolecular mass spectrometry and its instrumentation, capillary and microchip separation techniques, and microchemical manipulations of carbohydrate reactivity. The multimethodological approach appears to be necessary to gain an in-depth understanding of very complex glycomes in different biological systems.

Structural Glycomic Analyses at High Sensitivity: A Decade of Progress

PubMed Central

Alley, William R.; Novotny, Milos V.

2014-01-01

The field of glycomics has recently advanced in response to the urgent need for structural characterization and quantification of complex carbohydrates in biologically and medically important applications. The recent success of analytical glycobiology at high sensitivity reflects numerous advances in biomolecular mass spectrometry and its instrumentation, capillary and microchip separation techniques, and microchemical manipulations of carbohydrate reactivity. The multimethodological approach appears to be necessary to gain an in-depth understanding of very complex glycomes in different biological systems. PMID:23560930

Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology.

PubMed

Barah, Pankaj; Bones, Atle M

2015-02-01

The biggest challenge for modern biology is to integrate multidisciplinary approaches towards understanding the organizational and functional complexity of biological systems at different hierarchies, starting from the subcellular molecular mechanisms (microscopic) to the functional interactions of ecological communities (macroscopic). The plant-insect interaction is a good model for this purpose with the availability of an enormous amount of information at the molecular and the ecosystem levels. Changing global climatic conditions are abruptly resetting plant-insect interactions. Integration of discretely located heterogeneous information from the ecosystem to genes and pathways will be an advantage to understand the complexity of plant-insect interactions. This review will present the recent developments in omics-based high-throughput experimental approaches, with particular emphasis on studying plant defence responses against insect attack. The review highlights the importance of using integrative systems approaches to study plant-insect interactions from the macroscopic to the microscopic level. We analyse the current efforts in generating, integrating and modelling multiomics data to understand plant-insect interaction at a systems level. As a future prospect, we highlight the growing interest in utilizing the synthetic biology platform for engineering insect-resistant plants. © 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.

Pedagogy of complex relationality exploring complexity theory, neuro-phenomenology, and attentiveness for education

NASA Astrophysics Data System (ADS)

Khattar, Randa

What do the new sciences of complex relationality offer education? This work draws on complexity theory, neurological understandings of biology and phenomenology, and attentiveness to study what the new sciences might offer education and the possibilities of a pedagogical understanding of embodied knowing. Complexity theory provides understandings of local-global relationality, self-organization, far-from-equilibrium conditions, and emergent dynamics that are important for describing pedagogical relationality. In itself, however, complexity theory is lacking an attention to issues of embodiment that respond directly to an ethical understanding of relationality. Phenomenology provides important views on the human experience of perception, for example, Merleau-Ponty's, whose research opens up possibilities for embodiment and attentiveness. At the level of pedagogical practice, I will pose, following biologists Humberto Maturana and Francisco Varela's autopoietic self-making understanding of life, that attentiveness perspectives, which have been largely absent from western pedagogical theory and practice, are crucial to promoting embodied knowing for education. Maturana and Varela's autopoietic perspective offers an embodied understanding of living---and therefore education---which opens up necessary attentive spaces to listen to one another in non judgmental awareness in the present moment of experience. I offer insights into a relationally complex conception of education drawing on this biological and autopoietically-grounded framework. These insights are framed in the context of five clusters of relations: (1) emergence, far-from-equilibrium, and local-global relationality; (2) autopoietic autonomy, structural determination and sensory-motor coupling; (3) triggering perturbations, structural plasticity, and autopoietic organization; (4) knowing as adequate action, domains of interaction, and blind spots; and (5) attentiveness and responsive relationality. Four examples of pedagogical practices that are illustrative of these clusters are offered: the L'Arche Communities, the conditional teaching of the Mindfulness Research Lab, the purposeless assignment, and the progettazione of the Reggio Emilia approach to early childhood.

Using biotechnology and genomics to improve biotic and abiotic stress in apple

USDA-ARS?s Scientific Manuscript database

Genomic sequencing, molecular biology, and transformation technologies are providing valuable tools to better understand the complexity of how plants develop, function, and respond to biotic and abiotic stress. These approaches should complement but not replace a solid understanding of whole plant ...

Use of Graph Database for the Integration of Heterogeneous Biological Data.

PubMed

Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young

2017-03-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Use of Graph Database for the Integration of Heterogeneous Biological Data

PubMed Central

Yoon, Byoung-Ha; Kim, Seon-Kyu

2017-01-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. PMID:28416946

New and improved proteomics technologies for understanding complex biological systems: Addressing a grand challenge in the life sciences

PubMed Central

Hood, Leroy E.; Omenn, Gilbert S.; Moritz, Robert L.; Aebersold, Ruedi; Yamamoto, Keith R.; Amos, Michael; Hunter-Cevera, Jennie; Locascio, Laurie

2014-01-01

This White Paper sets out a Life Sciences Grand Challenge for Proteomics Technologies to enhance our understanding of complex biological systems, link genomes with phenotypes, and bring broad benefits to the biosciences and the US economy. The paper is based on a workshop hosted by the National Institute of Standards and Technology (NIST) in Gaithersburg, MD, 14–15 February 2011, with participants from many federal R&D agencies and research communities, under the aegis of the US National Science and Technology Council (NSTC). Opportunities are identified for a coordinated R&D effort to achieve major technology-based goals and address societal challenges in health, agriculture, nutrition, energy, environment, national security, and economic development. PMID:22807061

Tilting at Quixotic Trait Loci (QTL): An Evolutionary Perspective on Genetic Causation

PubMed Central

Weiss, Kenneth M.

2008-01-01

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity. PMID:18711218

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.

A next generation multiscale view of inborn errors of metabolism

PubMed Central

Argmann, Carmen A.; Houten, Sander M.; Zhu, Jun; Schadt, Eric E.

2015-01-01

Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this. PMID:26712461

Dynamical systems in economics

NASA Astrophysics Data System (ADS)

Stanojević, Jelena; Kukić, Katarina

2018-01-01

In last few decades much attention is given to explain complex behaviour of very large systems, such as weather, economy, biology and demography. In this paper we give short overview of basic notions in the field of dynamical systems which are relevant for understanding complex nature of some economic models.

Structure, Agency, Complexity Theory and Interdisciplinary Research in Education Studies

ERIC Educational Resources Information Center

Smith, John A.

2013-01-01

This article argues that Education Studies needs to develop its existing interdisciplinarity understanding of structures and agencies by giving greater attention to the modern process theories of self-organisation in the physical, biological, psychological and social sciences, sometimes given the umbrella term "complexity theory". The…

Biological determination of mental disorders: a discussion based on recent hypotheses from neuroscience.

PubMed

Freitas-Silva, Luna Rodrigues; Ortega, Francisco

2016-08-29

Understanding the processes involved in the development of mental disorders has proven challenging ever since psychiatry was founded as a field. Neuroscience has provided new expectations that an explanation will be found for the development of mental disorders based on biological functioning alone. However, such a goal has not been that easy to achieve, and new hypotheses have begun to appear in neuroscience research. In this article we identify epigenetics, neurodevelopment, and plasticity as the principal avenues for a new understanding of the biology of mental phenomena. Genetic complexity, the environment's formative role, and variations in vulnerability involve important changes in the principal hypotheses on biological determination of mental disorders, suggesting a reconfiguration of the limits between the "social" and the "biological" in neuroscience research.

Biological robustness.

PubMed

Kitano, Hiroaki

2004-11-01

Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

Investigating how students communicate tree-thinking

NASA Astrophysics Data System (ADS)

Boyce, Carrie Jo

Learning is often an active endeavor that requires students work at building conceptual understandings of complex topics. Personal experiences, ideas, and communication all play large roles in developing knowledge of and understanding complex topics. Sometimes these experiences can promote formation of scientifically inaccurate or incomplete ideas. Representations are tools used to help individuals understand complex topics. In biology, one way that educators help people understand evolutionary histories of organisms is by using representations called phylogenetic trees. In order to understand phylogenetics trees, individuals need to understand the conventions associated with phylogenies. My dissertation, supported by the Tree-Thinking Representational Competence and Word Association frameworks, is a mixed-methods study investigating the changes in students' tree-reading, representational competence and mental association of phylogenetic terminology after participation in varied instruction. Participants included 128 introductory biology majors from a mid-sized southern research university. Participants were enrolled in either Introductory Biology I, where they were not taught phylogenetics, or Introductory Biology II, where they were explicitly taught phylogenetics. I collected data using a pre- and post-assessment consisting of a word association task and tree-thinking diagnostic (n=128). Additionally, I recruited a subset of students from both courses (n=37) to complete a computer simulation designed to teach students about phylogenetic trees. I then conducted semi-structured interviews consisting of a word association exercise with card sort task, a retrospective pre-assessment discussion, a post-assessment discussion, and interview questions. I found that students who received explicit lecture instruction had a significantly higher increase in scores on a tree-thinking diagnostic than students who did not receive lecture instruction. Students who received both explicit lecture instruction and the computer simulation had a higher level of representational competence and were better able to understand abstract-style phylogenetic trees than students who only completed the simulation. Students who received explicit lecture instruction had a slightly more scientific association of phylogenetic terms than students who received did not receive lecture instruction. My findings suggest that technological instruction alone is not as beneficial as lecture instruction.

[A complexity analysis of Chinese herbal property theory: the multiple expressions of herbal property].

PubMed

Jin, Rui; Zhang, Bing

2012-12-01

Chinese herbal property is the highly summarized concept of herbal nature and pharmaceutical effect, which reflect the characteristics of herbal actions on human body. These herbal actions, also interpreted as presenting the information about pharmaceutical effect contained in herbal property on the biological carrier, are defined as herbal property expressions. However, the biological expression of herbal property is believed to possess complex features for the involved complexity of Chinese medicine and organism. Firstly, there are multiple factors which could influence the expression results of herbal property such as the growth environment, harvest season and preparing methods of medicinal herbs, and physique and syndrome of body. Secondly, there are multiple biological approaches and biochemical indicators for the expression of the same property. This paper elaborated these complexities for further understanding of herbal property. The individuality of herbs and expression factors should be well analyzed in the related studies.

Will systems biology offer new holistic paradigms to life sciences?

PubMed Central

Conti, Filippo; Valerio, Maria Cristina; Zbilut, Joseph P.

2008-01-01

A biological system, like any complex system, blends stochastic and deterministic features, displaying properties of both. In a certain sense, this blend is exactly what we perceive as the “essence of complexity” given we tend to consider as non-complex both an ideal gas (fully stochastic and understandable at the statistical level in the thermodynamic limit of a huge number of particles) and a frictionless pendulum (fully deterministic relative to its motion). In this commentary we make the statement that systems biology will have a relevant impact on nowadays biology if (and only if) will be able to capture the essential character of this blend that in our opinion is the generation of globally ordered collective modes supported by locally stochastic atomisms. PMID:19003440

Progress in bioinformatics and the importance of being earnest.

PubMed

Attwood, T K; Miller, C J

2002-01-01

In silico biology has gathered momentum as, worldwide, scientists have united in a common quest to sequence, store and analyse complete genomes. This year, a pivotal achievement of this cooperative endeavour was realised in the release of a public draft of the human genome, and with it the promises to improve our understanding of diverse aspects of biology and to yield a healthier future with safe personalized medicines. Key to these goals will be the need to elucidate and characterise the genes and gene products encoded not just in the human genome, but in many genomes. These tasks are underpinned by the concepts and processes of genome and gene/protein evolution, regulation of gene expression, mechanisms of protein folding, the manifestation of protein function, and so on, all of which must be understood in the context of complex, dynamic biological systems. Our use of computers to model such concepts and systems must be placed in the context of the current limits of our understanding of them:- it is important to recognise, for example, that we don't have a common understanding either of what constitutes a gene or a protein function; we can't invariably say that a particular sequence or fold has arisen via divergent or convergent evolution; and we don't fully understand the rules of protein folding. Accepting what we can't do in silico is essential in appreciating what we can do. Without this understanding, it is easy to be misled, as notions of what particular computational approaches can achieve are sometimes rather optimistic. There are valuable lessons to be learned here from the field of Artificial Intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. Thus, we argue here that if bioinformatics is to tackle biological complexity in earnest, it would be wise to absorb the experience distilled from decades of artificial intelligence research, and to approach the road ahead with caution, rigour and pragmatism.

Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

NASA Astrophysics Data System (ADS)

Christensen, Claire Petra

Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populations spanning two orders of magnitude are compared to prevaccine era measles data for England and Wales and demonstrate that the simulations are able to capture the quantitative and qualitative features of epidemics in populations as small as 10,000 people. The work presented in Chapter 5 validates the utility of network simulation in concurrently probing contact network dynamics and disease dynamics.

N-acetyltransferase single nucleotide polymorphisms: Emerging concepts serve as a paradigm for understanding complexities of personalized medicine

PubMed Central

Hein, David W.

2009-01-01

Arylamine N-acetyltransferase 1 (NAT1) and 2 (NAT2) exhibit single nucleotide polymorphisms (SNPs) in human populations that modify drug and carcinogen metabolism. This paper updates the identity, location, and functional effects of these SNPs and then follows with emerging concepts for understanding why pharmacogenetic findings may not be replicated consistently. Using this paradigm as an example, laboratory-based mechanistic analyses can reveal complexities such that genetic polymorphisms become biologically and medically relevant when confounding factors are more fully understood and considered. As medical care moves to a more personalized approach, the implications of these confounding factors will be important in understanding the complexities of personalized medicine. PMID:19379125

Testing Effect and Complex Comprehension in a Large Introductory Undergraduate Biology Course

ERIC Educational Resources Information Center

Pagliarulo, Christopher L.

2011-01-01

Traditional undergraduate biology courses are content intensive, requiring students to understand and remember large amounts of information in short periods of time. Yet most students maintain little of the material encountered during their education. Poor knowledge retention is a main cause of academic failure and high undergraduate attrition…

Advanced in situ Spectroscopic Techniques And Their Applications In Environmental Biogeochemistry: Introduction To The Special Section

EPA Science Inventory

Understanding the molecular-scale complexities and interplay of chemical and biological processes of contaminants at solid, liquid, and gas interfaces is a fundamental and crucial element to enhance our understanding of anthropogenic environmental impacts. The ability to describ...

Ask not what physics can do for biology—ask what biology can do for physics

NASA Astrophysics Data System (ADS)

Frauenfelder, Hans

2014-10-01

Stan Ulam, the famous mathematician, said once to Hans Frauenfelder: ‘Ask not what Physics can do for biology, ask what biology can do for physics’. The interaction between biologists and physicists is a two-way street. Biology reveals the secrets of complex systems, physics provides the physical tools and the theoretical concepts to understand the complexity. The perspective gives a personal view of the path to some of the physical concepts that are relevant for biology and physics (Frauenfelder et al 1999 Rev. Mod. Phys. 71 S419-S442). Schrödinger’s book (Schrödinger 1944 What is Life? (Cambridge: Cambridge University Press)), loved by physicists and hated by eminent biologists (Dronamraju 1999 Genetics 153 1071-6), still shows how a great physicist looked at biology well before the first protein structure was known.

Integrating multiple lines of evidence to assess biological hazards of complex mixtures: A case study in the Maumee River

EPA Science Inventory

Product Description:Due to technological improvements, increasing numbers of chemical contaminants are being detected in surface waters nation-wide, including the Great Lakes. Methods are needed to understand what impact these complex mixtures of contaminants can have on aquatic ...

From Complex to Simple: Interdisciplinary Stochastic Models

ERIC Educational Resources Information Center

Mazilu, D. A.; Zamora, G.; Mazilu, I.

2012-01-01

We present two simple, one-dimensional, stochastic models that lead to a qualitative understanding of very complex systems from biology, nanoscience and social sciences. The first model explains the complicated dynamics of microtubules, stochastic cellular highways. Using the theory of random walks in one dimension, we find analytical expressions…

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.

PubMed

Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh

2017-07-03

Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.

Six Classroom Exercises to Teach Natural Selection to Undergraduate Biology Students

PubMed Central

Kalinowski, Steven T.; Leonard, Mary J.; Andrews, Tessa M.; Litt, Andrea R.

2013-01-01

Students in introductory biology courses frequently have misconceptions regarding natural selection. In this paper, we describe six activities that biology instructors can use to teach undergraduate students in introductory biology courses how natural selection causes evolution. These activities begin with a lesson introducing students to natural selection and also include discussions on sexual selection, molecular evolution, evolution of complex traits, and the evolution of behavior. The set of six topics gives students the opportunity to see how natural selection operates in a variety of contexts. Pre- and postinstruction testing showed students’ understanding of natural selection increased substantially after completing this series of learning activities. Testing throughout this unit showed steadily increasing student understanding, and surveys indicated students enjoyed the activities. PMID:24006396

Complexity: the organizing principle at the interface of biological (dis)order.

PubMed

Bhat, Ramray; Pally, Dharma

2017-07-01

The term complexity means several things to biologists.When qualifying morphological phenotype, on the one hand, it is used to signify the sheer complicatedness of living systems, especially as a result of the multicomponent aspect of biological form. On the other hand, it has been used to represent the intricate nature of the connections between constituents that make up form: a more process-based explanation. In the context of evolutionary arguments, complexity has been defined, in a quantifiable fashion, as the amount of information, an informatic template such as a sequence of nucleotides or amino acids stores about its environment. In this perspective, we begin with a brief review of the history of complexity theory. We then introduce a developmental and an evolutionary understanding of what it means for biological systems to be complex.We propose that the complexity of living systems can be understood through two interdependent structural properties: multiscalarity of interconstituent mechanisms and excitability of the biological materials. The answer to whether a system becomes more or less complex over time depends on the potential for its constituents to interact in novel ways and combinations to give rise to new structures and functions, as well as on the evolution of excitable properties that would facilitate the exploration of interconstituent organization in the context of their microenvironments and macroenvironments.

Linear control theory for gene network modeling.

PubMed

Shin, Yong-Jun; Bleris, Leonidas

2010-09-16

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering☆

PubMed Central

Rabitz, Herschel; Welsh, William J.; Kohn, Joachim; de Boer, Jan

2016-01-01

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. PMID:26876875

Omics/systems biology and cancer cachexia.

PubMed

Gallagher, Iain J; Jacobi, Carsten; Tardif, Nicolas; Rooyackers, Olav; Fearon, Kenneth

2016-06-01

Cancer cachexia is a complex syndrome generated by interaction between the host and tumour cells with a background of treatment effects and toxicity. The complexity of the physiological pathways likely involved in cancer cachexia necessitates a holistic view of the relevant biology. Emergent properties are characteristic of complex systems with the result that the end result is more than the sum of its parts. Recognition of the importance of emergent properties in biology led to the concept of systems biology wherein a holistic approach is taken to the biology at hand. Systems biology approaches will therefore play an important role in work to uncover key mechanisms with therapeutic potential in cancer cachexia. The 'omics' technologies provide a global view of biological systems. Genomics, transcriptomics, proteomics, lipidomics and metabolomics approaches all have application in the study of cancer cachexia to generate systems level models of the behaviour of this syndrome. The current work reviews recent applications of these technologies to muscle atrophy in general and cancer cachexia in particular with a view to progress towards integration of these approaches to better understand the pathology and potential treatment pathways in cancer cachexia. Copyright © 2016. Published by Elsevier Ltd.

Quantitative biological surface science: challenges and recent advances.

PubMed

Höök, Fredrik; Kasemo, Bengt; Grunze, Michael; Zauscher, Stefan

2008-12-23

Biological surface science is a broad, interdisciplinary subfield of surface science, where properties and processes at biological and synthetic surfaces and interfaces are investigated, and where biofunctional surfaces are fabricated. The need to study and to understand biological surfaces and interfaces in liquid environments provides sizable challenges as well as fascinating opportunities. Here, we report on recent progress in biological surface science that was described within the program assembled by the Biomaterial Interface Division of the Science and Technology of Materials, Interfaces and Processes (www.avs.org) during their 55th International Symposium and Exhibition held in Boston, October 19-24, 2008. The selected examples show that the rapid progress in nanoscience and nanotechnology, hand-in-hand with theory and simulation, provides increasingly sophisticated methods and tools to unravel the mechanisms and details of complex processes at biological surfaces and in-depth understanding of biomolecular surface interactions.

Deconstructing Superorganisms and Societies to Address Big Questions in Biology.

PubMed

Kennedy, Patrick; Baron, Gemma; Qiu, Bitao; Freitak, Dalial; Helanterä, Heikki; Hunt, Edmund R; Manfredini, Fabio; O'Shea-Wheller, Thomas; Patalano, Solenn; Pull, Christopher D; Sasaki, Takao; Taylor, Daisy; Wyatt, Christopher D R; Sumner, Seirian

2017-11-01

Social insect societies are long-standing models for understanding social behaviour and evolution. Unlike other advanced biological societies (such as the multicellular body), the component parts of social insect societies can be easily deconstructed and manipulated. Recent methodological and theoretical innovations have exploited this trait to address an expanded range of biological questions. We illustrate the broadening range of biological insight coming from social insect biology with four examples. These new frontiers promote open-minded, interdisciplinary exploration of one of the richest and most complex of biological phenomena: sociality. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bioinformatics and Cancer

Cancer.gov

Researchers take on challenges and opportunities to mine "Big Data" for answers to complex biological questions. Learn how bioinformatics uses advanced computing, mathematics, and technological platforms to store, manage, analyze, and understand data.

Integrative systems and synthetic biology of cell-matrix adhesion sites.

PubMed

Zamir, Eli

2016-09-02

The complexity of cell-matrix adhesion convolves its roles in the development and functioning of multicellular organisms and their evolutionary tinkering. Cell-matrix adhesion is mediated by sites along the plasma membrane that anchor the actin cytoskeleton to the matrix via a large number of proteins, collectively called the integrin adhesome. Fundamental challenges for understanding how cell-matrix adhesion sites assemble and function arise from their multi-functionality, rapid dynamics, large number of components and molecular diversity. Systems biology faces these challenges in its strive to understand how the integrin adhesome gives rise to functional adhesion sites. Synthetic biology enables engineering intracellular modules and circuits with properties of interest. In this review I discuss some of the fundamental questions in systems biology of cell-matrix adhesion and how synthetic biology can help addressing them.

Does constructive neutral evolution play an important role in the origin of cellular complexity? Making sense of the origins and uses of biological complexity.

PubMed

Speijer, Dave

2011-05-01

Recently, constructive neutral evolution has been touted as an important concept for the understanding of the emergence of cellular complexity. It has been invoked to help explain the development and retention of, amongst others, RNA splicing, RNA editing and ribosomal and mitochondrial respiratory chain complexity. The theory originated as a welcome explanation of isolated small scale cellular idiosyncrasies and as a reaction to 'overselectionism'. Here I contend, that in its extended form, it has major conceptual problems, can not explain observed patterns of complex processes, is too easily dismissive of alternative selectionist models, underestimates the creative force of complexity as such, and--if seen as a major evolutionary mechanism for all organisms--could stifle further thought regarding the evolution of highly complex biological processes. Copyright © 2011 WILEY Periodicals, Inc.

Engineering of routes to heparin and related polysaccharides.

PubMed

Bhaskar, Ujjwal; Sterner, Eric; Hickey, Anne Marie; Onishi, Akihiro; Zhang, Fuming; Dordick, Jonathan S; Linhardt, Robert J

2012-01-01

Anticoagulant heparin has been shown to possess important biological functions that vary according to its fine structure. Variability within heparin's structure occurs owing to its biosynthesis and animal tissue-based recovery and adds another dimension to its complex polymeric structure. The structural variations in chain length and sulfation patterns mediate its interaction with many heparin-binding proteins, thereby eliciting complex biological responses. The advent of novel chemical and enzymatic approaches for polysaccharide synthesis coupled with high throughput combinatorial approaches for drug discovery have facilitated an increased effort to understand heparin's structure-activity relationships. An improved understanding would offer potential for new therapeutic development through the engineering of polysaccharides. Such a bioengineering approach requires the amalgamation of several different disciplines, including carbohydrate synthesis, applied enzymology, metabolic engineering, and process biochemistry.

Understanding the nanoparticle-protein corona complexes using computational and experimental methods.

PubMed

Kharazian, B; Hadipour, N L; Ejtehadi, M R

2016-06-01

Nanoparticles (NP) have capability to adsorb proteins from biological fluids and form protein layer, which is called protein corona. As the cell sees corona coated NPs, the protein corona can dictate biological response to NPs. The composition of protein corona is varied by physicochemical properties of NPs including size, shape, surface chemistry. Processing of protein adsorption is dynamic phenomena; to that end, a protein may desorb or leave a surface vacancy that is rapidly filled by another protein and cause changes in the corona composition mainly by the Vroman effect. In this review, we discuss the interaction between NP and proteins and the available techniques for identification of NP-bound proteins. Also we review current developed computational methods for understanding the NP-protein complex interactions. Copyright © 2016. Published by Elsevier Ltd.

New and improved proteomics technologies for understanding complex biological systems: addressing a grand challenge in the life sciences.

PubMed

Hood, Leroy E; Omenn, Gilbert S; Moritz, Robert L; Aebersold, Ruedi; Yamamoto, Keith R; Amos, Michael; Hunter-Cevera, Jennie; Locascio, Laurie

2012-09-01

This White Paper sets out a Life Sciences Grand Challenge for Proteomics Technologies to enhance our understanding of complex biological systems, link genomes with phenotypes, and bring broad benefits to the biosciences and the US economy. The paper is based on a workshop hosted by the National Institute of Standards and Technology (NIST) in Gaithersburg, MD, 14-15 February 2011, with participants from many federal R&D agencies and research communities, under the aegis of the US National Science and Technology Council (NSTC). Opportunities are identified for a coordinated R&D effort to achieve major technology-based goals and address societal challenges in health, agriculture, nutrition, energy, environment, national security, and economic development. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Network Approach to Disease Diagnosis

NASA Astrophysics Data System (ADS)

Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

2014-03-01

Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

KRAS Mouse Models

PubMed Central

O’Hagan, Rónán C.; Heyer, Joerg

2011-01-01

KRAS is a potent oncogene and is mutated in about 30% of all human cancers. However, the biological context of KRAS-dependent oncogenesis is poorly understood. Genetically engineered mouse models of cancer provide invaluable tools to study the oncogenic process, and insights from KRAS-driven models have significantly increased our understanding of the genetic, cellular, and tissue contexts in which KRAS is competent for oncogenesis. Moreover, variation among tumors arising in mouse models can provide insight into the mechanisms underlying response or resistance to therapy in KRAS-dependent cancers. Hence, it is essential that models of KRAS-driven cancers accurately reflect the genetics of human tumors and recapitulate the complex tumor-stromal intercommunication that is manifest in human cancers. Here, we highlight the progress made in modeling KRAS-dependent cancers and the impact that these models have had on our understanding of cancer biology. In particular, the development of models that recapitulate the complex biology of human cancers enables translational insights into mechanisms of therapeutic intervention in KRAS-dependent cancers. PMID:21779503

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes.

PubMed

Srihari, Sriganesh; Yong, Chern Han; Patil, Ashwini; Wong, Limsoon

2015-09-14

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area. Copyright © 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

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

PubMed

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

2009-03-01

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

Tangible Models and Haptic Representations Aid Learning of Molecular Biology Concepts

ERIC Educational Resources Information Center

Johannes, Kristen; Powers, Jacklyn; Couper, Lisa; Silberglitt, Matt; Davenport, Jodi

2016-01-01

Can novel 3D models help students develop a deeper understanding of core concepts in molecular biology? We adapted 3D molecular models, developed by scientists, for use in high school science classrooms. The models accurately represent the structural and functional properties of complex DNA and Virus molecules, and provide visual and haptic…

Can a biologist fix a smartphone?-Just hack it!

PubMed

Kamoun, Sophien

2017-05-08

Biological systems integrate multiscale processes and networks and are, therefore, viewed as difficult to dissect. However, because of the clear-cut separation between the software code (the information encoded in the genome sequence) and hardware (organism), genome editors can operate as software engineers to hack biological systems without any particularly deep understanding of the complexity of the systems.

Epigenetic Effects of Diet on Fruit Fly Lifespan: An Investigation to Teach Epigenetics to Biology Students

ERIC Educational Resources Information Center

Billingsley, James; Carlson, Kimberly A.

2010-01-01

Do our genes exclusively control us, or are other factors at play? Epigenetics can provide a means for students to use inquiry-based methods to understand a complex biological concept. Students research and design an experiment testing whether dietary supplements affect the lifespan of Drosophila melanogaster over multiple generations.

Red Onions, "Elodea," or Decalcified Chicken Eggs? Selecting & Sequencing Representations for Teaching Diffusion & Osmosis

ERIC Educational Resources Information Center

Lankford, Deanna; Friedrichsen, Patricia

2012-01-01

Diffusion and osmosis are important biological concepts that students often struggle to understand. These are important concepts because they are the basis for many complex biological processes, such as photosynthesis and cellular respiration. We examine a wide variety of representations used by experienced teachers to teach diffusion and osmosis.…

The Role of Biomarkers in the Assessment of Aquatic Ecosystem Health

PubMed Central

Hook, Sharon E; Gallagher, Evan P; Batley, Graeme E

2016-01-01

Ensuring the health of aquatic ecosystems and identifying species at risk from the detrimental effects of environmental contaminants can be facilitated by integrating analytical chemical analysis with carefully selected biological endpoints measured in tissues of species of concern. These biological endpoints include molecular, biochemical and physiological markers (i.e. biomarkers) that when integrated, can clarify issues of contaminant bioavailability, bioaccumulation and ecological effects while enabling a better understanding of the effects of non-chemical stressors. In the case of contaminant stressors, an understanding of chemical modes of toxicity can be incorporated with diagnostic markers of aquatic animal physiology to help understand the health status of aquatic organisms in the field. Furthermore, new approaches in functional genomics and bioinformatics can help discriminate individual chemicals, or groups of chemicals among complex mixtures that may contribute to adverse biological effects. While the use of biomarkers is not a new paradigm, such approaches have been underutilized in the context of ecological risk assessment and natural resource damage assessment. From a regulatory standpoint, these approaches can help better assess the complex effects from coastal development activities to assessing ecosystem integrity pre- and post-development or site remediation. PMID:24574147

An integrative model of evolutionary covariance: a symposium on body shape in fishes.

PubMed

Walker, Jeffrey A

2010-12-01

A major direction of current and future biological research is to understand how multiple, interacting functional systems coordinate in producing a body that works. This understanding is complicated by the fact that organisms need to work well in multiple environments, with both predictable and unpredictable environmental perturbations. Furthermore, organismal design reflects a history of past environments and not a plan for future environments. How complex, interacting functional systems evolve, then, is a truly grand challenge. In accepting the challenge, an integrative model of evolutionary covariance is developed. The model combines quantitative genetics, functional morphology/physiology, and functional ecology. The model is used to convene scientists ranging from geneticists, to physiologists, to ecologists, to engineers to facilitate the emergence of body shape in fishes as a model system for understanding how complex, interacting functional systems develop and evolve. Body shape of fish is a complex morphology that (1) results from many developmental paths and (2) functions in many different behaviors. Understanding the coordination and evolution of the many paths from genes to body shape, body shape to function, and function to a working fish body in a dynamic environment is now possible given new technologies from genetics to engineering and new theoretical models that integrate the different levels of biological organization (from genes to ecology).

Governability Framework for the Evaluation and Implementation of Complex Public Health Functions

ERIC Educational Resources Information Center

Varghese, Joe; Kutty, V. Raman

2012-01-01

Background: The dominant theoretical basis of our public health practice originates from a positivist or reductionist paradigm. It fails to take into account the complexity emerging out of public health's multiple influences originating from biological and social worlds. A deeper understanding of the interaction of elements that characterize the…

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Computational structure analysis of biomacromolecule complexes by interface geometry.

PubMed

Mahdavi, Sedigheh; Salehzadeh-Yazdi, Ali; Mohades, Ali; Masoudi-Nejad, Ali

2013-12-01

The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model. Copyright © 2013 Elsevier Ltd. All rights reserved.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

PubMed

Popescu, George V; Noutsos, Christos; Popescu, Sorina C

2016-01-01

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.

PubMed

Macrae, Calum A

2010-07-01

IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease biology, but emphasizes the importance of the rigor of the disease model.

Using an adaptive expertise lens to understand the quality of teachers' classroom implementation of computer-supported complex systems curricula in high school science

NASA Astrophysics Data System (ADS)

Yoon, Susan A.; Koehler-Yom, Jessica; Anderson, Emma; Lin, Joyce; Klopfer, Eric

2015-05-01

Background: This exploratory study is part of a larger-scale research project aimed at building theoretical and practical knowledge of complex systems in students and teachers with the goal of improving high school biology learning through professional development and a classroom intervention. Purpose: We propose a model of adaptive expertise to better understand teachers' classroom practices as they attempt to navigate myriad variables in the implementation of biology units that include working with computer simulations, and learning about and teaching through complex systems ideas. Sample: Research participants were three high school biology teachers, two females and one male, ranging in teaching experience from six to 16 years. Their teaching contexts also ranged in student achievement from 14-47% advanced science proficiency. Design and methods: We used a holistic multiple case study methodology and collected data during the 2011-2012 school year. Data sources include classroom observations, teacher and student surveys, and interviews. Data analyses and trustworthiness measures were conducted through qualitative mining of data sources and triangulation of findings. Results: We illustrate the characteristics of adaptive expertise of more or less successful teaching and learning when implementing complex systems curricula. We also demonstrate differences between case study teachers in terms of particular variables associated with adaptive expertise. Conclusions: This research contributes to scholarship on practices and professional development needed to better support teachers to teach through a complex systems pedagogical and curricular approach.

Understanding Protein Synthesis: An Interactive Card Game Discussion

ERIC Educational Resources Information Center

Lewis, Alison; Peat, Mary; Franklin, Sue

2005-01-01

Protein synthesis is a complex process and students find it difficult to understand. This article describes an interactive discussion "game" used by first year biology students at the University of Sydney. The students, in small groups, use the game in which the processes of protein synthesis are actioned by the students during a…

Advances in biotechnology and linking outputs to variation in complex traits: Plant and Animal Genome meeting January 2012.

PubMed

Appels, R; Barrero, R; Bellgard, M

2012-03-01

The Plant and Animal Genome (PAG, held annually) meeting in January 2012 provided insights into the advances in plant, animal, and microbe genome studies particularly as they impact on our understanding of complex biological systems. The diverse areas of biology covered included the advances in technologies, variation in complex traits, genome change in evolution, and targeting phenotypic changes, across the broad spectrum of life forms. This overview aims to summarize the major advances in research areas presented in the plenary lectures and does not attempt to summarize the diverse research activities covered throughout the PAG in workshops, posters, presentations, and displays by suppliers of cutting-edge technologies.

Beyond disease susceptibility-Leveraging genome-wide association studies for new insights into complex disease biology.

PubMed

Lee, J C

2017-12-01

Genetic studies in complex diseases have been highly successful, but have also been largely one-dimensional: predominantly focusing on the genetic contribution to disease susceptibility. While this is undoubtedly important-indeed it is a pre-requisite for understanding the mechanisms underlying disease development-there are many other important aspects of disease biology that have received comparatively little attention. In this review, I will discuss how existing genetic data can be leveraged to provide new insights into other aspects of disease biology, why such insights could change the way we think about complex disease, and how this could provide opportunities for better therapies and/or facilitate personalised medicine. To do this, I will use the example of Crohn's disease-a chronic form of inflammatory bowel disease that has been one of the main success stories in complex disease genetics. Indeed, thanks to genetic studies, we now have a much more detailed understanding of the processes involved in Crohn's disease development, but still know relatively little about what determines the subsequent disease course (prognosis) and why this differs so considerably between individuals. I will discuss how we came to realise that genetic variation plays an important role in determining disease prognosis and how this has changed the way we think about Crohn's disease genetics. This will illustrate how phenotypic data can be used to leverage new insights from genetic data and will provide a broadly applicable framework that could yield new insights into the biology of multiple diseases. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Getting a Grip on Complexes

PubMed Central

Nie, Yan; Viola, Cristina; Bieniossek, Christoph; Trowitzsch, Simon; Vijay-achandran, Lakshmi Sumitra; Chaillet, Maxime; Garzoni, Frederic; Berger, Imre

2009-01-01

We are witnessing tremendous advances in our understanding of the organization of life. Complete genomes are being deciphered with ever increasing speed and accuracy, thereby setting the stage for addressing the entire gene product repertoire of cells, towards understanding whole biological systems. Advances in bioinformatics and mass spectrometric techniques have revealed the multitude of interactions present in the proteome. Multiprotein complexes are emerging as a paramount cornerstone of biological activity, as many proteins appear to participate, stably or transiently, in large multisubunit assemblies. Analysis of the architecture of these assemblies and their manifold interactions is imperative for understanding their function at the molecular level. Structural genomics efforts have fostered the development of many technologies towards achieving the throughput required for studying system-wide single proteins and small interaction motifs at high resolution. The present shift in focus towards large multiprotein complexes, in particular in eukaryotes, now calls for a likewise concerted effort to develop and provide new technologies that are urgently required to produce in quality and quantity the plethora of multiprotein assemblies that form the complexome, and to routinely study their structure and function at the molecular level. Current efforts towards this objective are summarized and reviewed in this contribution. PMID:20514218

The Biophysics Microgravity Initiative

NASA Technical Reports Server (NTRS)

Gorti, S.

2016-01-01

Biophysical microgravity research on the International Space Station using biological materials has been ongoing for several decades. The well-documented substantive effects of long duration microgravity include the facilitation of the assembly of biological macromolecules into large structures, e.g., formation of large protein crystals under micro-gravity. NASA is invested not only in understanding the possible physical mechanisms of crystal growth, but also promoting two flight investigations to determine the influence of µ-gravity on protein crystal quality. In addition to crystal growth, flight investigations to determine the effects of shear on nucleation and subsequent formation of complex structures (e.g., crystals, fibrils, etc.) are also supported. It is now considered that long duration microgravity research aboard the ISS could also make possible the formation of large complex biological and biomimetic materials. Investigations of various materials undergoing complex structure formation in microgravity will not only strengthen NASA science programs, but may also provide invaluable insight towards the construction of large complex tissues, organs, or biomimetic materials on Earth.

Applying systems biology methods to the study of human physiology in extreme environments

PubMed Central

2013-01-01

Systems biology is defined in this review as ‘an iterative process of computational model building and experimental model revision with the aim of understanding or simulating complex biological systems’. We propose that, in practice, systems biology rests on three pillars: computation, the omics disciplines and repeated experimental perturbation of the system of interest. The number of ethical and physiologically relevant perturbations that can be used in experiments on healthy humans is extremely limited and principally comprises exercise, nutrition, infusions (e.g. Intralipid), some drugs and altered environment. Thus, we argue that systems biology and environmental physiology are natural symbionts for those interested in a system-level understanding of human biology. However, despite excellent progress in high-altitude genetics and several proteomics studies, systems biology research into human adaptation to extreme environments is in its infancy. A brief description and overview of systems biology in its current guise is given, followed by a mini review of computational methods used for modelling biological systems. Special attention is given to high-altitude research, metabolic network reconstruction and constraint-based modelling. PMID:23849719

The Contribution of Novel Brain Imaging Techniques to Understanding the Neurobiology of Mental Retardation and Developmental Disabilities

ERIC Educational Resources Information Center

Gothelf, Doron; Furfaro, Joyce A.; Penniman, Lauren C.; Glover, Gary H.; Reiss, Allan L.

2005-01-01

Studying the biological mechanisms underlying mental retardation and developmental disabilities (MR/DD) is a very complex task. This is due to the wide heterogeneity of etiologies and pathways that lead to MR/DD. Breakthroughs in genetics and molecular biology and the development of sophisticated brain imaging techniques during the last decades…

The Oral Histories of Six African American Males in Their Ecology of Advanced Placement Biology

ERIC Educational Resources Information Center

Halasa, Katrina Bassam

2012-01-01

The major purpose of this qualitative study was to examine the past in order to understand the complex phenomenon of students engaging in science (Newman, Ridenour, Newman, & DeMarco, 2003) specifically through the oral histories of six self-identified African American males enrolled in a high school Advanced Placement Biology class and the…

Biocharts: a visual formalism for complex biological systems

PubMed Central

Kugler, Hillel; Larjo, Antti; Harel, David

2010-01-01

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis. PMID:20022895

Novel insights into an old disease: recent developments in scabies mite biology.

PubMed

Holt, Deborah C; Fischer, Katja

2013-04-01

Scabies is a serious disease of both humans and other animals caused by infestation of the skin with the ectoparasitic mite Sarcoptes scabiei. Our current understanding of scabies mite biology and disease processes is far outweighed by the significant, worldwide impact of the disease. This review summarizes the recent data which furthers our knowledge of mite biology, host specificity and parasite host evasion mechanisms. Recent data concords with the previous work demonstrating limited gene flow between different host-associated populations of scabies mites. This evidence of the host specificity of scabies mites has important implications for disease control programmes. Other studies have begun to decipher the molecular basis of the complex host-parasite interactions underlying scabies infestations. Scabies mites have developed complex mechanisms to interfere with the host defence processes that may also enhance the survival of the associated skin microbiome, consistent with the epidemiological evidence. Recently developed natural host models of scabies are valuable tools to further study the disease processes and to trial novel therapeutic agents. Although significant progress has been made, further research is needed to understand the biology, host-parasite interactions and pathogenesis of this ubiquitous parasite.

Synchronization in complex networks

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

Arenas, A.; Diaz-Guilera, A.; Moreno, Y.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analyticalmore » approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.« less

Connections Matter: Social Networks and Lifespan Health in Primate Translational Models

PubMed Central

McCowan, Brenda; Beisner, Brianne; Bliss-Moreau, Eliza; Vandeleest, Jessica; Jin, Jian; Hannibal, Darcy; Hsieh, Fushing

2016-01-01

Humans live in societies full of rich and complex relationships that influence health. The ability to improve human health requires a detailed understanding of the complex interplay of biological systems that contribute to disease processes, including the mechanisms underlying the influence of social contexts on these biological systems. A longitudinal computational systems science approach provides methods uniquely suited to elucidate the mechanisms by which social systems influence health and well-being by investigating how they modulate the interplay among biological systems across the lifespan. In the present report, we argue that nonhuman primate social systems are sufficiently complex to serve as model systems allowing for the development and refinement of both analytical and theoretical frameworks linking social life to health. Ultimately, developing systems science frameworks in nonhuman primate models will speed discovery of the mechanisms that subserve the relationship between social life and human health. PMID:27148103

Using simple manipulatives to improve student comprehension of a complex biological process: protein synthesis.

PubMed

Guzman, Karen; Bartlett, John

2012-01-01

Biological systems and living processes involve a complex interplay of biochemicals and macromolecular structures that can be challenging for undergraduate students to comprehend and, thus, misconceptions abound. Protein synthesis, or translation, is an example of a biological process for which students often hold many misconceptions. This article describes an exercise that was developed to illustrate the process of translation using simple objects to represent complex molecules. Animations, 3D physical models, computer simulations, laboratory experiments and classroom lectures are also used to reinforce the students' understanding of translation, but by focusing on the simple manipulatives in this exercise, students are better able to visualize concepts that can elude them when using the other methods. The translation exercise is described along with suggestions for background material, questions used to evaluate student comprehension and tips for using the manipulatives to identify common misconceptions. Copyright © 2012 Wiley Periodicals, Inc.

Life under the Microscope: Single-Molecule Fluorescence Highlights the RNA World.

PubMed

Ray, Sujay; Widom, Julia R; Walter, Nils G

2018-04-25

The emergence of single-molecule (SM) fluorescence techniques has opened up a vast new toolbox for exploring the molecular basis of life. The ability to monitor individual biomolecules in real time enables complex, dynamic folding pathways to be interrogated without the averaging effect of ensemble measurements. In parallel, modern biology has been revolutionized by our emerging understanding of the many functions of RNA. In this comprehensive review, we survey SM fluorescence approaches and discuss how the application of these tools to RNA and RNA-containing macromolecular complexes in vitro has yielded significant insights into the underlying biology. Topics covered include the three-dimensional folding landscapes of a plethora of isolated RNA molecules, their assembly and interactions in RNA-protein complexes, and the relation of these properties to their biological functions. In all of these examples, the use of SM fluorescence methods has revealed critical information beyond the reach of ensemble averages.

Application of Nuclear Magnetic Resonance and Hybrid Methods to Structure Determination of Complex Systems.

PubMed

Prischi, Filippo; Pastore, Annalisa

2016-01-01

The current main challenge of Structural Biology is to undertake the structure determination of increasingly complex systems in the attempt to better understand their biological function. As systems become more challenging, however, there is an increasing demand for the parallel use of more than one independent technique to allow pushing the frontiers of structure determination and, at the same time, obtaining independent structural validation. The combination of different Structural Biology methods has been named hybrid approaches. The aim of this review is to critically discuss the most recent examples and new developments that have allowed structure determination or experimentally-based modelling of various molecular complexes selecting them among those that combine the use of nuclear magnetic resonance and small angle scattering techniques. We provide a selective but focused account of some of the most exciting recent approaches and discuss their possible further developments.

Proof of Concept: A review on how network and systems biology approaches aid in the discovery of potent anticancer drug combinations

PubMed Central

Azmi, Asfar S.; Wang, Zhiwei; Philip, Philip A.; Mohammad, Ramzi M.; Sarkar, Fazlul H.

2010-01-01

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignances. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. This requires innovative approaches such as understanding the global physiological environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases where efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology has emerged as a powerful tool benefiting our understanding of drug mechanism of action in real time. This mini-review highlights the significance of the network and systems biology-based strategy and presents a “proof-of-concept” recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma. PMID:21041384

Emerging themes in the ecology and management of North American forests

Treesearch

Terry L. Sharik; William Adair; Fred A. Baker; Michael Battaglia; Emily J. Comfort; Anthony W. D' Amato; Craig Delong; R. Justin DeRose; Mark J. Ducey; Mark Harmon; Louise Levy; Jesse A. Logan; Joseph O' Brien; Brian J. Palik; Scott D. Roberts; Paul C. Rogers; Douglas J. Shinneman; Thomas Spies; Sarah L. Taylor; Christopher Woodall; Andrew Youngblood

2010-01-01

Forests are extremely complex systems that respond to an overwhelming number of biological and environmental factors, which can act singularly and in concert with each other, as exemplified by Puettmann et al. [1]. The complexity of forest systems presents an enormous challenge for forest researchers who try to deepen their understanding of the structure and function...

Understanding Global Change (UGC) as a Unifying Conceptual Framework for Teaching Ecology: Using UGC in a High School Biology Program to Integrate Earth Science and Biology, and to Demonstrate the Value of Modeling Global Systems in Promoting Conceptual Learning

NASA Astrophysics Data System (ADS)

Levine, J.; Bean, J. R.

2017-12-01

Global change science is ideal for NGSS-informed teaching, but presents a serious challenge to K-12 educators because it is complex and interdisciplinary- combining earth science, biology, chemistry, and physics. Global systems are themselves complex. Adding anthropogenic influences on those systems creates a formidable list of topics - greenhouse effect, climate change, nitrogen enrichment, introduced species, land-use change among them - which are often presented as a disconnected "laundry list" of "facts." This complexity, combined with public and mass-media scientific illiteracy, leaves global change science vulnerable to misrepresentation and politicization, creating additional challenges to teachers in public schools. Ample stand-alone, one-off, online resources, many of them excellent, are (to date) underutilized by teachers in the high school science course taken by most students: biology. The Understanding Global Change project (UGC) from the UC Berkeley Museum of Paleontology has created a conceptual framework that organizes, connects, and explains global systems, human and non-human drivers of change in those systems, and measurable changes in those systems. This organization and framework employ core ideas, crosscutting concepts, structure/function relationships, and system models in a unique format that facilitates authentic understanding, rather than memorization. This system serves as an organizing framework for the entire ecology unit of a forthcoming mainstream high school biology program. The UGC system model is introduced up front with its core informational graphic. The model is elaborated, step by step, by adding concepts and processes as they are introduced and explained in each chapter. The informational graphic is thus used in several ways: to organize material as it is presented, to summarize topics in each chapter and put them in perspective, and for review and critical thinking exercises that supplement the usual end-of-chapter lists of key terms.

Modeling of the U1 snRNP assembly pathway in alternative splicing in human cells using Petri nets.

PubMed

Kielbassa, J; Bortfeldt, R; Schuster, S; Koch, I

2009-02-01

The investigation of spliceosomal processes is currently a topic of intense research in molecular biology. In the molecular mechanism of alternative splicing, a multi-protein-RNA complex - the spliceosome - plays a crucial role. To understand the biological processes of alternative splicing, it is essential to comprehend the biogenesis of the spliceosome. In this paper, we propose the first abstract model of the regulatory assembly pathway of the human spliceosomal subunit U1. Using Petri nets, we describe its highly ordered assembly that takes place in a stepwise manner. Petri net theory represents a mathematical formalism to model and analyze systems with concurrent processes at different abstraction levels with the possibility to combine them into a uniform description language. There exist many approaches to determine static and dynamic properties of Petri nets, which can be applied to analyze biochemical systems. In addition, Petri net tools usually provide intuitively understandable graphical network representations, which facilitate the dialog between experimentalists and theoreticians. Our Petri net model covers binding, transport, signaling, and covalent modification processes. Through the computation of structural and behavioral Petri net properties and their interpretation in biological terms, we validate our model and use it to get a better understanding of the complex processes of the assembly pathway. We can explain the basic network behavior, using minimal T-invariants which represent special pathways through the network. We find linear as well as cyclic pathways. We determine the P-invariants that represent conserved moieties in a network. The simulation of the net demonstrates the importance of the stability of complexes during the maturation pathway. We can show that complexes that dissociate too fast, hinder the formation of the complete U1 snRNP.

Bioinformatics of cardiovascular miRNA biology.

PubMed

Kunz, Meik; Xiao, Ke; Liang, Chunguang; Viereck, Janika; Pachel, Christina; Frantz, Stefan; Thum, Thomas; Dandekar, Thomas

2015-12-01

MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs and are highly conserved among species. Moreover, miRNAs regulate gene expression of a large number of genes associated with important biological functions and signaling pathways. Recently, several miRNAs have been found to be associated with cardiovascular diseases. Thus, investigating the complex regulatory effect of miRNAs may lead to a better understanding of their functional role in the heart. To achieve this, bioinformatics approaches have to be coupled with validation and screening experiments to understand the complex interactions of miRNAs with the genome. This will boost the subsequent development of diagnostic markers and our understanding of the physiological and therapeutic role of miRNAs in cardiac remodeling. In this review, we focus on and explain different bioinformatics strategies and algorithms for the identification and analysis of miRNAs and their regulatory elements to better understand cardiac miRNA biology. Starting with the biogenesis of miRNAs, we present approaches such as LocARNA and miRBase for combining sequence and structure analysis including phylogenetic comparisons as well as detailed analysis of RNA folding patterns, functional target prediction, signaling pathway as well as functional analysis. We also show how far bioinformatics helps to tackle the unprecedented level of complexity and systemic effects by miRNA, underlining the strong therapeutic potential of miRNA and miRNA target structures in cardiovascular disease. In addition, we discuss drawbacks and limitations of bioinformatics algorithms and the necessity of experimental approaches for miRNA target identification. This article is part of a Special Issue entitled 'Non-coding RNAs'. Copyright © 2014 Elsevier Ltd. All rights reserved.

In Touch with Molecules: Improving Student Learning with Innovative Molecular Models

ERIC Educational Resources Information Center

Davenport, Jodi; Silberglitt, Matt; Olson, Arthur

2013-01-01

How do viruses self-assemble? Why do DNA bases pair the way they do? What factors determine whether strands of proteins fold into sheets or helices? Why does handedness matter? A deep understanding of core issues in biology requires students to understand both complex spatial structures of molecules and the interactions involved in dynamic…

Race and Ethnicity in the Genome Era: The Complexity of the Constructs

ERIC Educational Resources Information Center

Bonham, Vence L.; Warshauer-Baker, Esther; Collins, Francis S.

2005-01-01

The vast amount of biological information that is now available through the completion of the Human Genome Project presents opportunities and challenges. The genomic era has the potential to advance an understanding of human genetic variation and its role in human health and disease. A challenge for genomics research is to understand the…

Understanding RNA modifications: the promises and technological bottlenecks of the ‘epitranscriptome’

PubMed Central

Kapoor, Utkarsh

2017-01-01

The discovery of mechanisms that alter genetic information via RNA editing or introducing covalent RNA modifications points towards a complexity in gene expression that challenges long-standing concepts. Understanding the biology of RNA modifications represents one of the next frontiers in molecular biology. To this date, over 130 different RNA modifications have been identified, and improved mass spectrometry approaches are still adding to this list. However, only recently has it been possible to map selected RNA modifications at single-nucleotide resolution, which has created a number of exciting hypotheses about the biological function of RNA modifications, culminating in the proposition of the ‘epitranscriptome’. Here, we review some of the technological advances in this rapidly developing field, identify the conceptual challenges and discuss approaches that are needed to rigorously test the biological function of specific RNA modifications. PMID:28566301

Quantitative biology of single neurons

PubMed Central

Eberwine, James; Lovatt, Ditte; Buckley, Peter; Dueck, Hannah; Francis, Chantal; Kim, Tae Kyung; Lee, Jaehee; Lee, Miler; Miyashiro, Kevin; Morris, Jacqueline; Peritz, Tiina; Schochet, Terri; Spaethling, Jennifer; Sul, Jai-Yoon; Kim, Junhyong

2012-01-01

The building blocks of complex biological systems are single cells. Fundamental insights gained from single-cell analysis promise to provide the framework for understanding normal biological systems development as well as the limits on systems/cellular ability to respond to disease. The interplay of cells to create functional systems is not well understood. Until recently, the study of single cells has concentrated primarily on morphological and physiological characterization. With the application of new highly sensitive molecular and genomic technologies, the quantitative biochemistry of single cells is now accessible. PMID:22915636

25 Years of Tension over Actin Binding to the Cadherin Cell Adhesion Complex: The Devil is in the Details.

PubMed

Nelson, W James; Weis, William I

2016-07-01

Over the past 25 years, there has been a conceptual (re)evolution in understanding how the cadherin cell adhesion complex, which contains F-actin-binding proteins, binds to the actin cytoskeleton. There is now good synergy between structural, biochemical, and cell biological results that the cadherin-catenin complex binds to F-actin under force. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tumor-induced perturbations of cytokines and immune cell networks.

PubMed

Burkholder, Brett; Huang, Ren-Yu; Burgess, Rob; Luo, Shuhong; Jones, Valerie Sloane; Zhang, Wenji; Lv, Zhi-Qiang; Gao, Chang-Yu; Wang, Bao-Ling; Zhang, Yu-Ming; Huang, Ruo-Pan

2014-04-01

Until recently, the intrinsically high level of cross-talk between immune cells, the complexity of immune cell development, and the pleiotropic nature of cytokine signaling have hampered progress in understanding the mechanisms of immunosuppression by which tumor cells circumvent native and adaptive immune responses. One technology that has helped to shed light on this complex signaling network is the cytokine antibody array, which facilitates simultaneous screening of dozens to hundreds of secreted signal proteins in complex biological samples. The combined applications of traditional methods of molecular and cell biology with the high-content, high-throughput screening capabilities of cytokine antibody arrays and other multiplexed immunoassays have revealed a complex mechanism that involves multiple cytokine signals contributed not just by tumor cells but by stromal cells and a wide spectrum of immune cell types. This review will summarize the interactions among cancerous and immune cell types, as well as the key cytokine signals that are required for tumors to survive immunoediting in a dormant state or to grow and spread by escaping it. Additionally, it will present examples of how probing secreted cell-cell signal networks in the tumor microenvironment (TME) with cytokine screens have contributed to our current understanding of these processes and discuss the implications of this understanding to antitumor therapies. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Mapping Proteome-Wide Interactions of Reactive Chemicals Using Chemoproteomic Platforms

PubMed Central

Counihan, Jessica L.; Ford, Breanna; Nomura, Daniel K.

2015-01-01

A large number of pharmaceuticals, endogenous metabolites, and environmental chemicals act through covalent mechanisms with protein targets. Yet, their specific interactions with the proteome still remain poorly defined for most of these reactive chemicals. Deciphering direct protein targets of reactive small-molecules is critical in understanding their biological action, off-target effects, potential toxicological liabilities, and development of safer and more selective agents. Chemoproteomic technologies have arisen as a powerful strategy that enable the assessment of proteome-wide interactions of these irreversible agents directly in complex biological systems. We review here several chemoproteomic strategies that have facilitated our understanding of specific protein interactions of irreversibly-acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms. PMID:26647369

Understanding the immune response to seasonal influenza vaccination in older adults: a systems biology approach.

PubMed

Lambert, Nathaniel D; Ovsyannikova, Inna G; Pankratz, V Shane; Jacobson, Robert M; Poland, Gregory A

2012-08-01

Annual vaccination against seasonal influenza is recommended to decrease disease-related mortality and morbidity. However, one population that responds suboptimally to influenza vaccine is adults over the age of 65 years. The natural aging process is associated with a complex deterioration of multiple components of the host immune system. Research into this phenomenon, known as immunosenescence, has shown that aging alters both the innate and adaptive branches of the immune system. The intricate mechanisms involved in immune response to influenza vaccine, and how these responses are altered with age, have led us to adopt a more encompassing systems biology approach to understand exactly why the response to vaccination diminishes with age. Here, the authors review what changes occur with immunosenescence, and some immunogenetic factors that influence response, and outline the systems biology approach to understand the immune response to seasonal influenza vaccination in older adults.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering.

PubMed

Groen, Nathalie; Guvendiren, Murat; Rabitz, Herschel; Welsh, William J; Kohn, Joachim; de Boer, Jan

2016-04-01

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. Copyright © 2016. Published by Elsevier Ltd.

The effect of multiple external representations (MERs) worksheets toward complex system reasoning achievement

NASA Astrophysics Data System (ADS)

Sumarno; Ibrahim, M.; Supardi, Z. A. I.

2018-03-01

The application of a systems approach to assessing biological systems provides hope for a coherent understanding of cell dynamics patterns and their relationship to plant life. This action required the reasoning about complex systems. In other sides, there were a lot of researchers who provided the proof about the instructional successions. They involved the multiple external representations which improved the biological learning. The researcher conducted an investigation using one shoot case study design which involved 30 students in proving that the MERs worksheets could affect the student's achievement of reasoning about complex system. The data had been collected based on test of reasoning about complex system and student's identification result who worked through MERs. The result showed that only partially students could achieve reasoning about system complex, but their MERs skill could support their reasoning ability of complex system. This study could bring a new hope to develop the MERs worksheet as a tool to facilitate the reasoning about complex system.

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

NASA Astrophysics Data System (ADS)

Stoop, Ruedi; Gomez, Florian

2016-07-01

The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

Soil biology research across latitude, elevation and disturbance gradients: A review of forest studies from Puerto Rico during the past 25 years

Treesearch

Grizelle González; D. Lodge

2017-01-01

Progress in understanding changes in soil biology in response to latitude, elevation and disturbance gradients has generally lagged behind studies of above-ground plants and animals owing to methodological constraints and high diversity and complexity of interactions in below-ground food webs. New methods have opened research opportunities in below-ground systems,...

Emerging concepts for management of river ecosystems and challenges to applied integration of physical and biological sciences in the Pacific Northwest, USA

Treesearch

Bruce E. Rieman; Jason B. Dunham; James L. Clayton

2006-01-01

Integration of biological and physical concepts is necessary to understand and conserve the ecological integrity of river systems. Past attempts at integration have often focused at relatively small scales and on mechanistic models that may not capture the complexity of natural systems leaving substantial uncertainty about ecological responses to management actions....

Systems biology: A tool for charting the antiviral landscape.

PubMed

Bowen, James R; Ferris, Martin T; Suthar, Mehul S

2016-06-15

The host antiviral programs that are initiated following viral infection form a dynamic and complex web of responses that we have collectively termed as "the antiviral landscape". Conventional approaches to studying antiviral responses have primarily used reductionist systems to assess the function of a single or a limited subset of molecules. Systems biology is a holistic approach that considers the entire system as a whole, rather than individual components or molecules. Systems biology based approaches facilitate an unbiased and comprehensive analysis of the antiviral landscape, while allowing for the discovery of emergent properties that are missed by conventional approaches. The antiviral landscape can be viewed as a hierarchy of complexity, beginning at the whole organism level and progressing downward to isolated tissues, populations of cells, and single cells. In this review, we will discuss how systems biology has been applied to better understand the antiviral landscape at each of these layers. At the organismal level, the Collaborative Cross is an invaluable genetic resource for assessing how genetic diversity influences the antiviral response. Whole tissue and isolated bulk cell transcriptomics serves as a critical tool for the comprehensive analysis of antiviral responses at both the tissue and cellular levels of complexity. Finally, new techniques in single cell analysis are emerging tools that will revolutionize our understanding of how individual cells within a bulk infected cell population contribute to the overall antiviral landscape. Copyright © 2016 Elsevier B.V. All rights reserved.

The trajectory of life. Decreasing physiological network complexity through changing fractal patterns

PubMed Central

Sturmberg, Joachim P.; Bennett, Jeanette M.; Picard, Martin; Seely, Andrew J. E.

2015-01-01

In this position paper, we submit a synthesis of theoretical models based on physiology, non-equilibrium thermodynamics, and non-linear time-series analysis. Based on an understanding of the human organism as a system of interconnected complex adaptive systems, we seek to examine the relationship between health, complexity, variability, and entropy production, as it might be useful to help understand aging, and improve care for patients. We observe the trajectory of life is characterized by the growth, plateauing and subsequent loss of adaptive function of organ systems, associated with loss of functioning and coordination of systems. Understanding development and aging requires the examination of interdependence among these organ systems. Increasing evidence suggests network interconnectedness and complexity can be captured/measured/associated with the degree and complexity of healthy biologic rhythm variability (e.g., heart and respiratory rate variability). We review physiological mechanisms linking the omics, arousal/stress systems, immune function, and mitochondrial bioenergetics; highlighting their interdependence in normal physiological function and aging. We argue that aging, known to be characterized by a loss of variability, is manifested at multiple scales, within functional units at the small scale, and reflected by diagnostic features at the larger scale. While still controversial and under investigation, it appears conceivable that the integrity of whole body complexity may be, at least partially, reflected in the degree and variability of intrinsic biologic rhythms, which we believe are related to overall system complexity that may be a defining feature of health and it's loss through aging. Harnessing this information for the development of therapeutic and preventative strategies may hold an opportunity to significantly improve the health of our patients across the trajectory of life. PMID:26082722

Ways of incorporating photographic images in learning and assessing high school biology: A study of visual perception and visual cognition

NASA Astrophysics Data System (ADS)

Nixon, Brenda Chaumont

This study evaluated the cognitive benefits and costs of incorporating biology-textbook and student-generated photographic images into the learning and assessment processes within a 10th grade biology classroom. The study implemented Wandersee's (2000) 20-Q Model of Image-Based Biology Test-Item Design (20-Q Model) to explore the use of photographic images to assess students' understanding of complex biological processes. A thorough review of the students' textbook using ScaleMaster R with PC Interface was also conducted. The photographs, diagrams, and other representations found in the textbook were measured to determine the percentage of each graphic depicted in the book and comparisons were made to the text. The theoretical framework that guided the research included Human Constructivist tenets espoused by Mintzes, Wandersee and Novak (2000). Physiological and cognitive factors of images and image-based learning as described by Robin (1992), Solso (1997) and Wandersee (2000) were examined. Qualitative case study design presented by Yin (1994), Denzin and Lincoln (1994) was applied and data were collected through interviews, observations, student activities, student and school artifacts and Scale Master IIRTM measurements. The results of the study indicate that although 24% of the high school biology textbook is devoted to photographic images which contribute significantly to textbook cost, the teacher and students paid little attention to photographic images other than as aesthetic elements for creating biological ambiance, wasting valuable opportunities for learning. The analysis of the photographs corroborated findings published by the Association American Association for the Advancement of Science that indicated "While most of the books are lavishly illustrated, these representations are rarely helpful, because they are too abstract, needlessly complicated, or inadequately explained" (Roseman, 2000, p. 2). The findings also indicate that applying the 20-Q Model to photographs in biology textbooks can (a) effectively assess students' understanding of complex biological concepts, (b) offer alternative assessment strategies that complement individual learning styles, (c) identify misconceptions, and (d) encourage students to practice metacognition. In addition, once students have learned how to interpret textbook images, application of that knowledge through self-generated biologically relevant digital or print images provides opportunities for increased conceptual understanding.

A Genome-Wide Association Study of Autism Incorporating Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, and Social Responsiveness Scale

ERIC Educational Resources Information Center

Connolly, John J.; Glessner, Joseph T.; Hakonarson, Hakon

2013-01-01

Efforts to understand the causes of autism spectrum disorders (ASDs) have been hampered by genetic complexity and heterogeneity among individuals. One strategy for reducing complexity is to target endophenotypes, simpler biologically based measures that may involve fewer genes and constitute a more homogenous sample. A genome-wide association…

Introductory Biology Students’ Use of Enhanced Answer Keys and Reflection Questions to Engage in Metacognition and Enhance Understanding

PubMed Central

Sabel, Jaime L.; Dauer, Joseph T.; Forbes, Cory T.

2017-01-01

Providing feedback to students as they learn to integrate individual concepts into complex systems is an important way to help them to develop robust understanding, but it is challenging in large, undergraduate classes for instructors to provide feedback that is frequent and directed enough to help individual students. Various scaffolds can be used to help students engage in self-regulated learning and generate internal feedback to improve their learning. This study examined the use of enhanced answer keys with added reflection questions and instruction as scaffolds for engaging undergraduate students in self-regulated learning within an introductory biology course. Study findings show that both the enhanced answer keys and reflection questions helped students to engage in metacognition and develop greater understanding of biological concepts. Further, students who received additional instruction on the use of the scaffolds changed how they used them and, by the end of the semester, were using the scaffolds in significantly different ways and showed significantly higher learning gains than students who did not receive the instruction. These findings provide evidence for the benefit of designing scaffolds within biology courses that will support students in engaging in metacognition and enhancing their understanding of biological concepts. PMID:28645893

Regulation of drug-metabolizing enzymes in infectious and inflammatory disease: implications for biologics-small molecule drug interactions.

PubMed

Mallick, Pankajini; Taneja, Guncha; Moorthy, Bhagavatula; Ghose, Romi

2017-06-01

Drug-metabolizing enzymes (DMEs) are primarily down-regulated during infectious and inflammatory diseases, leading to disruption in the metabolism of small molecule drugs (smds), which are increasingly being prescribed therapeutically in combination with biologics for a number of chronic diseases. The biologics may exert pro- or anti-inflammatory effect, which may in turn affect the expression/activity of DMEs. Thus, patients with infectious/inflammatory diseases undergoing biologic/smd treatment can have complex changes in DMEs due to combined effects of the disease and treatment. Areas covered: We will discuss clinical biologics-SMD interaction and regulation of DMEs during infection and inflammatory diseases. Mechanistic studies will be discussed and consequences on biologic-small molecule combination therapy on disease outcome due to changes in drug metabolism will be highlighted. Expert opinion: The involvement of immunomodulatory mediators in biologic-SMDs is well known. Regulatory guidelines recommend appropriate in vitro or in vivo assessments for possible interactions. The role of cytokines in biologic-SMDs has been documented. However, the mechanisms of drug-drug interactions is much more complex, and is probably multi-factorial. Studies aimed at understanding the mechanism by which biologics effect the DMEs during inflammation/infection are clinically important.

The Promise of Systems Biology Approaches for Revealing Host Pathogen Interactions in Malaria

PubMed Central

Zuck, Meghan; Austin, Laura S.; Danziger, Samuel A.; Aitchison, John D.; Kaushansky, Alexis

2017-01-01

Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a “call to arms” for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication. PMID:29201016

CHEMICAL PROCESSES AND MODELING IN ECOSYSTEMS

EPA Science Inventory

Trends in regulatory strategies require EPA to understand better chemical behavior in natural and impacted ecosystems and in biological systems to carry out the increasingly complex array of exposure and risk assessments needed to develop scientifically defensible regulations (GP...

Telomere biology in aging and cancer: early history and perspectives.

PubMed

Hayashi, Makoto T

2018-01-20

The ends of eukaryotic linear chromosomes are protected from undesired enzymatic activities by a nucleoprotein complex called the telomere. Expanding evidence indicates that telomeres have central functions in human aging and tumorigenesis. While it is undoubtedly important to follow current advances in telomere biology, it is also fruitful to be well informed in seminal historical studies for a comprehensive understanding of telomere biology, and for the anticipation of future directions. With this in mind, I here summarize the early history of telomere biology and current advances in the field, mostly focusing on mammalian studies relevant to aging and cancer.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology.

PubMed

Rao, Rohit T; Scherholz, Megerle L; Hartmanshenn, Clara; Bae, Seul-A; Androulakis, Ioannis P

2017-12-05

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

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.

Biological and Clinical Aspects of Lanthanide Coordination Compounds

PubMed Central

Misra, Sudhindra N.; M., Indira Devi; Shukla, Ram S.

2004-01-01

The coordinating chemistry of lanthanides, relevant to the biological, biochemical and medical aspects, makes a significant contribution to understanding the basis of application of lanthanides, particularly in biological and medical systems. The importance of the applications of lanthanides, as an excellent diagnostic and prognostic probe in clinical diagnostics, and an anticancer material, is remarkably increasing. Lanthanide complexes based X-ray contrast imaging and lanthanide chelates based contrast enhancing agents for magnetic resonance imaging (MRI) are being excessively used in radiological analysis in our body systems. The most important property of the chelating agents, in lanthanide chelate complex, is its ability to alter the behaviour of lanthanide ion with which it binds in biological systems, and the chelation markedly modifies the biodistribution and excretion profile of the lanthanide ions. The chelating agents, especially aminopoly carboxylic acids, being hydrophilic, increase the proportion of their complex excreted from complexed lanthanide ion form biological systems. Lanthanide polyamino carboxylate-chelate complexes are used as contrast enhancing agents for Magnetic Resonance Imaging. Conjugation of antibodies and other tissue specific molecules to lanthanide chelates has led to a new type of specific MRI contrast agents and their conjugated MRI contrast agents with improved relaxivity, functioning in the body similar to drugs. Many specific features of contrast agent assisted MRI make it particularly effective for musculoskeletal and cerebrospinal imaging. Lanthanide-chelate contrast agents are effectively used in clinical diagnostic investigations involving cerebrospinal diseases and in evaluation of central nervous system. Chelated lanthanide complexes shift reagent aided 23Na NMR spectroscopic analysis is used in cellular, tissue and whole organ systems. PMID:18365075

Principles of assembly reveal a periodic table of protein complexes.

PubMed

Ahnert, Sebastian E; Marsh, Joseph A; Hernández, Helena; Robinson, Carol V; Teichmann, Sarah A

2015-12-11

Structural insights into protein complexes have had a broad impact on our understanding of biological function and evolution. In this work, we sought a comprehensive understanding of the general principles underlying quaternary structure organization in protein complexes. We first examined the fundamental steps by which protein complexes can assemble, using experimental and structure-based characterization of assembly pathways. Most assembly transitions can be classified into three basic types, which can then be used to exhaustively enumerate a large set of possible quaternary structure topologies. These topologies, which include the vast majority of observed protein complex structures, enable a natural organization of protein complexes into a periodic table. On the basis of this table, we can accurately predict the expected frequencies of quaternary structure topologies, including those not yet observed. These results have important implications for quaternary structure prediction, modeling, and engineering. Copyright © 2015, American Association for the Advancement of Science.

Systems biology-based approaches toward understanding drought tolerance in food crops.

PubMed

Jogaiah, Sudisha; Govind, Sharathchandra Ramsandra; Tran, Lam-Son Phan

2013-03-01

Economically important crops, such as maize, wheat, rice, barley, and other food crops are affected by even small changes in water potential at important growth stages. Developing a comprehensive understanding of host response to drought requires a global view of the complex mechanisms involved. Research on drought tolerance has generally been conducted using discipline-specific approaches. However, plant stress response is complex and interlinked to a point where discipline-specific approaches do not give a complete global analysis of all the interlinked mechanisms. Systems biology perspective is needed to understand genome-scale networks required for building long-lasting drought resistance. Network maps have been constructed by integrating multiple functional genomics data with both model plants, such as Arabidopsis thaliana, Lotus japonicus, and Medicago truncatula, and various food crops, such as rice and soybean. Useful functional genomics data have been obtained from genome-wide comparative transcriptome and proteome analyses of drought responses from different crops. This integrative approach used by many groups has led to identification of commonly regulated signaling pathways and genes following exposure to drought. Combination of functional genomics and systems biology is very useful for comparative analysis of other food crops and has the ability to develop stable food systems worldwide. In addition, studying desiccation tolerance in resurrection plants will unravel how combination of molecular genetic and metabolic processes interacts to produce a resurrection phenotype. Systems biology-based approaches have helped in understanding how these individual factors and mechanisms (biochemical, molecular, and metabolic) "interact" spatially and temporally. Signaling network maps of such interactions are needed that can be used to design better engineering strategies for improving drought tolerance of important crop species.

Toward a multiscale modeling framework for understanding serotonergic function

PubMed Central

Wong-Lin, KongFatt; Wang, Da-Hui; Moustafa, Ahmed A; Cohen, Jeremiah Y; Nakamura, Kae

2017-01-01

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin. PMID:28417684

The social neuroscience and the theory of integrative levels.

PubMed

Bello-Morales, Raquel; Delgado-García, José María

2015-01-01

The theory of integrative levels provides a general description of the evolution of matter through successive orders of complexity and integration. Along its development, material forms pass through different levels of organization, such as physical, chemical, biological or sociological. The appearance of novel structures and dynamics during this process of development of matter in complex systems has been called emergence. Social neuroscience (SN), an interdisciplinary field that aims to investigate the biological mechanisms that underlie social structures, processes, and behavior and the influences between social and biological levels of organization, has affirmed the necessity for including social context as an essential element to understand the human behavior. To do this, SN proposes a multilevel integrative approach by means of three principles: multiple determinism, nonadditive determinism and reciprocal determinism. These theoretical principles seem to share the basic tenets of the theory of integrative levels but, in this paper, we aim to reveal the differences among both doctrines. First, SN asserts that combination of neural and social variables can produce emergent phenomena that would not be predictable from a neuroscientific or social psychological analysis alone; SN also suggests that to achieve a complete understanding of social structures we should use an integrative analysis that encompasses levels of organization ranging from the genetic level to the social one; finally, SN establishes that there can be mutual influences between biological and social factors in determining behavior, accepting, therefore, a double influence, upward from biology to social level, and downward, from social level to biology. In contrast, following the theory of integrative levels, emergent phenomena are not produced by the combination of variables from two levels, but by the increment of complexity at one level. In addition, the social behavior and structures might be contemplated not as the result of mixing or summing social and biological influences, but as emergent phenomena that should be described with its own laws. Finally, following the integrative levels view, influences upward, from biology to social level, and downward, from social level to biology, might not be equivalent, since the bottom-up processes are emergent and the downward causation (DC) is not.

The social neuroscience and the theory of integrative levels

PubMed Central

Bello-Morales, Raquel; Delgado-García, José María

2015-01-01

The theory of integrative levels provides a general description of the evolution of matter through successive orders of complexity and integration. Along its development, material forms pass through different levels of organization, such as physical, chemical, biological or sociological. The appearance of novel structures and dynamics during this process of development of matter in complex systems has been called emergence. Social neuroscience (SN), an interdisciplinary field that aims to investigate the biological mechanisms that underlie social structures, processes, and behavior and the influences between social and biological levels of organization, has affirmed the necessity for including social context as an essential element to understand the human behavior. To do this, SN proposes a multilevel integrative approach by means of three principles: multiple determinism, nonadditive determinism and reciprocal determinism. These theoretical principles seem to share the basic tenets of the theory of integrative levels but, in this paper, we aim to reveal the differences among both doctrines. First, SN asserts that combination of neural and social variables can produce emergent phenomena that would not be predictable from a neuroscientific or social psychological analysis alone; SN also suggests that to achieve a complete understanding of social structures we should use an integrative analysis that encompasses levels of organization ranging from the genetic level to the social one; finally, SN establishes that there can be mutual influences between biological and social factors in determining behavior, accepting, therefore, a double influence, upward from biology to social level, and downward, from social level to biology. In contrast, following the theory of integrative levels, emergent phenomena are not produced by the combination of variables from two levels, but by the increment of complexity at one level. In addition, the social behavior and structures might be contemplated not as the result of mixing or summing social and biological influences, but as emergent phenomena that should be described with its own laws. Finally, following the integrative levels view, influences upward, from biology to social level, and downward, from social level to biology, might not be equivalent, since the bottom-up processes are emergent and the downward causation (DC) is not. PMID:26578909

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

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

Nicora, Carrie D.; Burnum-Johnson, Kristin E.; Nakayasu, Ernesto S.

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for simultaneous metabolite, protein, and lipid extraction (MPLEx) from the exact same soil sample was created bymore » adapting a solvent-based approach. This MPLEx protocol has proven to be simple yet robust for many sample types and even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.« less

Toward an integrated software platform for systems pharmacology

PubMed Central

Ghosh, Samik; Matsuoka, Yukiko; Asai, Yoshiyuki; Hsin, Kun-Yi; Kitano, Hiroaki

2013-01-01

Understanding complex biological systems requires the extensive support of computational tools. This is particularly true for systems pharmacology, which aims to understand the action of drugs and their interactions in a systems context. Computational models play an important role as they can be viewed as an explicit representation of biological hypotheses to be tested. A series of software and data resources are used for model development, verification and exploration of the possible behaviors of biological systems using the model that may not be possible or not cost effective by experiments. Software platforms play a dominant role in creativity and productivity support and have transformed many industries, techniques that can be applied to biology as well. Establishing an integrated software platform will be the next important step in the field. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd. PMID:24150748

Identifying protein complexes in PPI network using non-cooperative sequential game.

PubMed

Maulik, Ujjwal; Basu, Srinka; Ray, Sumanta

2017-08-21

Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

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.

Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-A review.

PubMed

Boyd, Philip W; Collins, Sinead; Dupont, Sam; Fabricius, Katharina; Gattuso, Jean-Pierre; Havenhand, Jonathan; Hutchins, David A; Riebesell, Ulf; Rintoul, Max S; Vichi, Marcello; Biswas, Haimanti; Ciotti, Aurea; Gao, Kunshan; Gehlen, Marion; Hurd, Catriona L; Kurihara, Haruko; McGraw, Christina M; Navarro, Jorge M; Nilsson, Göran E; Passow, Uta; Pörtner, Hans-Otto

2018-06-01

Marine life is controlled by multiple physical and chemical drivers and by diverse ecological processes. Many of these oceanic properties are being altered by climate change and other anthropogenic pressures. Hence, identifying the influences of multifaceted ocean change, from local to global scales, is a complex task. To guide policy-making and make projections of the future of the marine biosphere, it is essential to understand biological responses at physiological, evolutionary and ecological levels. Here, we contrast and compare different approaches to multiple driver experiments that aim to elucidate biological responses to a complex matrix of ocean global change. We present the benefits and the challenges of each approach with a focus on marine research, and guidelines to navigate through these different categories to help identify strategies that might best address research questions in fundamental physiology, experimental evolutionary biology and community ecology. Our review reveals that the field of multiple driver research is being pulled in complementary directions: the need for reductionist approaches to obtain process-oriented, mechanistic understanding and a requirement to quantify responses to projected future scenarios of ocean change. We conclude the review with recommendations on how best to align different experimental approaches to contribute fundamental information needed for science-based policy formulation. © 2018 John Wiley & Sons Ltd.

Top-down models in biology: explanation and control of complex living systems above the molecular level.

PubMed

Pezzulo, Giovanni; Levin, Michael

2016-11-01

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering. © 2016 The Author(s).

Top-down models in biology: explanation and control of complex living systems above the molecular level

PubMed Central

2016-01-01

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering. PMID:27807271

Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Loppini, Alessandro

2018-03-01

Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

PubMed

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-08-10

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

A molecular signaling approach to linking intraspecific variation and macro-evolutionary patterns.

PubMed

Swanson, Eli M; Snell-Rood, Emilie C

2014-11-01

Macro-evolutionary comparisons are a valued tool in evolutionary biology. Nevertheless, our understanding of how systems involved in molecular signaling change in concert with phenotypic diversification has lagged. We argue that integrating our understanding of the evolution of molecular signaling systems with phylogenetic comparative methods is an important step toward understanding the processes linking variation among individuals with variation among species. Focusing mostly on the endocrine system, we discuss how the complexity and mechanistic nature of molecular signaling systems may influence the application and interpretation of macro-evolutionary comparisons. We also detail five hypotheses concerning the role that physiological mechanisms can play in shaping macro-evolutionary patterns, and discuss ways in which these hypotheses could influence phenotypic diversification. Finally, we review a series of tools able to analyze the complexity of physiological systems and the way they change in concert with the phenotypes for which they coordinate development. © 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.

Biological contributions to addictions in adolescents and adults: prevention, treatment, and policy implications.

PubMed

Potenza, Marc N

2013-02-01

Despite significant advances in our understanding of the biological bases of addictions, these disorders continue to represent a huge public health burden that is associated with substantial personal suffering. Efforts to target addictions require consideration of how the improved biological understanding of addictions may lead to improved prevention, treatment, and policy initiatives. In this article, we provide a narrative review of current biological models for addictions with a goal of placing existing data and theories within a translational and developmental framework targeting the advancement of prevention, treatment, and policy strategies. Data regarding individual differences, intermediary phenotypes, and main and interactive influences of genetic and environmental contributions in the setting of developmental trajectories that may be influenced by addictive drugs or behavior indicate complex underpinnings of addictions. Consideration and further elucidation of the biological etiologies of addictions hold significant potential for making important gains and reducing the public health impact of addictions. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Biological Contributions to Addictions in Adolescents and Adults: Prevention, Treatment and Policy Implications

PubMed Central

Potenza, Marc N.

2012-01-01

Purpose Despite significant advances in our understanding of the biological bases of addictions, these disorders continue to represent a huge public health burden that is associated with substantial personal suffering. Efforts to target addictions require consideration of how the improved biological understanding of addictions may lead to improved prevention, treatment and policy initiatives. Method In this article, we provide a narrative review of current biological models for addictions with a goal of placing existing data and theories within a translational and developmental framework targeting the advancement of prevention, treatment and policy strategies. Results Data regarding individual differences, intermediary phenotypes, and main and interactive influences of genetic and environmental contributions in the setting of developmental trajectories that may be influenced by addictive drugs or behavior indicate complex underpinnings of addictions. Conclusions Consideration and further elucidation of the biological etiologies of addictions hold significant potential for making important gains and reducing the public health impact of addictions. PMID:23332567

Using graph theory to analyze biological networks

PubMed Central

2011-01-01

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. PMID:21527005

In Silico Analysis for the Study of Botulinum Toxin Structure

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2010-01-01

Protein-protein interactions play many important roles in biological function. Knowledge of protein-protein complex structure is required for understanding the function. The determination of protein-protein complex structure by experimental studies remains difficult, therefore computational prediction of protein structures by structure modeling and docking studies is valuable method. In addition, MD simulation is also one of the most popular methods for protein structure modeling and characteristics. Here, we attempt to predict protein-protein complex structure and property using some of bioinformatic methods, and we focus botulinum toxin complex as target structure.

Using an Adaptive Expertise Lens to Understand the Quality of Teachers' Classroom Implementation of Computer-Supported Complex Systems Curricula in High School Science

ERIC Educational Resources Information Center

Yoon, Susan A.; Koehler-Yom, Jessica; Anderson, Emma; Lin, Joyce; Klopfer, Eric

2015-01-01

Background: This exploratory study is part of a larger-scale research project aimed at building theoretical and practical knowledge of complex systems in students and teachers with the goal of improving high school biology learning through professional development and a classroom intervention. Purpose: We propose a model of adaptive expertise to…

The Species Problem and the Value of Teaching and the Complexities of Species

ERIC Educational Resources Information Center

Chung, Carl

2004-01-01

Discussions on species taxa directly refer to a range of complex biological phenomena. Given these phenomena, biologists have developed and continue to appeal to a series of species concepts and do not have a clear definition for it as each species concept tells us part of the story or helps the biologists to explain and understand a subset of…

Mechanochemical cell biology.

PubMed

Cross, R A; McAinsh, A D; Straube, A

2011-12-01

Eukaryotic systems self-organise by using molecular railways to shuttle specific sets of molecular components to specific locations. In this way, cells are enabled to become larger, more complex and more varied, subtle and effective in their activities. Because of the fundamental importance of molecular railways in eukaryotic systems, understanding how these railways work is an important research goal. Mechanochemical cell biology is a newly circumscribed subject area that concerns itself with the molecular and cell biological mechanisms of motorised directional transport in living systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Programming Morphogenesis through Systems and Synthetic Biology.

PubMed

Velazquez, Jeremy J; Su, Emily; Cahan, Patrick; Ebrahimkhani, Mo R

2018-04-01

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.

Designing and testing a classroom curriculum to teach preschoolers about the biology of physical activity: The respiration system as an underlying biological causal mechanism

NASA Astrophysics Data System (ADS)

Ewing, Tracy S.

The present study examined young children's understanding of respiration and oxygen as a source of vital energy underlying physical activity. Specifically, the purpose of the study was to explore whether a coherent biological theory, characterized by an understanding that bodily parts (heart and lungs) and processes (oxygen in respiration) as part of a biological system, can be taught as a foundational concept to reason about physical activity. The effects of a biology-based intervention curriculum designed to teach preschool children about bodily functions as a part of the respiratory system, the role of oxygen as a vital substance and how physical activity acts an energy source were examined. Participants were recruited from three private preschool classrooms (two treatment; 1 control) in Southern California and included a total of 48 four-year-old children (30 treatment; 18 control). Findings from this study suggested that young children could be taught relevant biological concepts about the role of oxygen in respiratory processes. Children who received biology-based intervention curriculum made significant gains in their understanding of the biology of respiration, identification of physical and sedentary activities. In addition these children demonstrated that coherence of conceptual knowledge was correlated with improved accuracy at activity identification and reasoning about the inner workings of the body contributing to endurance. Findings from this study provided evidence to support the benefits of providing age appropriate but complex coherent biological instruction to children in early childhood settings.

Inflammasome complexes: emerging mechanisms and effector functions

PubMed Central

Rathinam, Vijay A. K.; Fitzgerald, Katherine A.

2017-01-01

Canonical activation of the inflammasome is critical to promote caspase-1-dependent maturation of the proinflammatory cytokines IL-1β and IL-18, as well as to induce pyroptotic cell death in response to pathogens and endogenous danger signals. Recent discoveries, however, are beginning to unveil new components of the inflammasome machinery, and the full spectrum of inflammasome functions, extending their influence beyond canonical functions, to regulation of eicosanoid storm, autophagy and metabolism. In addition, the receptor components of the inflammasome can also regulate diverse biological processes, such as cellular proliferation, gene transcription and tumorigenesis, all of which are independent of their inflammasome complex-forming capabilities. Here, we review these recent advances that are shaping our understanding of the complex biology of the inflammasome and its constituents. PMID:27153493

Using machine learning tools to model complex toxic interactions with limited sampling regimes.

PubMed

Bertin, Matthew J; Moeller, Peter; Guillette, Louis J; Chapman, Robert W

2013-03-19

A major impediment to understanding the impact of environmental stress, including toxins and other pollutants, on organisms, is that organisms are rarely challenged by one or a few stressors in natural systems. Thus, linking laboratory experiments that are limited by practical considerations to a few stressors and a few levels of these stressors to real world conditions is constrained. In addition, while the existence of complex interactions among stressors can be identified by current statistical methods, these methods do not provide a means to construct mathematical models of these interactions. In this paper, we offer a two-step process by which complex interactions of stressors on biological systems can be modeled in an experimental design that is within the limits of practicality. We begin with the notion that environment conditions circumscribe an n-dimensional hyperspace within which biological processes or end points are embedded. We then randomly sample this hyperspace to establish experimental conditions that span the range of the relevant parameters and conduct the experiment(s) based upon these selected conditions. Models of the complex interactions of the parameters are then extracted using machine learning tools, specifically artificial neural networks. This approach can rapidly generate highly accurate models of biological responses to complex interactions among environmentally relevant toxins, identify critical subspaces where nonlinear responses exist, and provide an expedient means of designing traditional experiments to test the impact of complex mixtures on biological responses. Further, this can be accomplished with an astonishingly small sample size.

A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

PubMed Central

Khalil, Ahmad S.; Lu, Timothy K.; Bashor, Caleb J.; Ramirez, Cherie L.; Pyenson, Nora C.; Joung, J. Keith; Collins, James J.

2013-01-01

SUMMARY Eukaryotic transcription factors (TFs) perform complex and combinatorial functions within transcriptional networks. Here, we present a synthetic framework for systematically constructing eukaryotic transcription functions using artificial zinc fingers, modular DNA-binding domains found within many eukaryotic TFs. Utilizing this platform, we construct a library of orthogonal synthetic transcription factors (sTFs) and use these to wire synthetic transcriptional circuits in yeast. We engineer complex functions, such as tunable output strength and transcriptional cooperativity, by rationally adjusting a decomposed set of key component properties, e.g., DNA specificity, affinity, promoter design, protein-protein interactions. We show that subtle perturbations to these properties can transform an individual sTF between distinct roles (activator, cooperative factor, inhibitory factor) within a transcriptional complex, thus drastically altering the signal processing behavior of multi-input systems. This platform provides new genetic components for synthetic biology and enables bottom-up approaches to understanding the design principles of eukaryotic transcriptional complexes and networks. PMID:22863014

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens. | Office of Cancer Genomics

Cancer.gov

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity.

Dynamism & Detail

ERIC Educational Resources Information Center

Flannery, Maura C.

2004-01-01

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

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

Moving college students to a better understanding of substrate specificity of enzymes through utilizing multimedia pre-training and an interactive enzyme model

NASA Astrophysics Data System (ADS)

Saleh, Mounir R.

Scientists' progress in understanding enzyme specificity uncovered a complex natural phenomenon. However, not all of the currently available biology textbooks seem to be up to date on this progress. Students' understanding of how enzymes work is a core requirement in biochemistry and biology tertiary education. Nevertheless, current pre-college science education does not provide students with enough biochemical background to enable them to understand complex material such as this. To bridge this gap, a multimedia pre-training presentation was prepared to fuel the learner's prior knowledge with discrete facts necessary to understand the presented concept. This treatment is also known to manage intrinsic cognitive load during the learning process. An interactive instructional enzyme model was also built to motivate students to learn about substrate specificity of enzymes. Upon testing the effect of this combined treatment on 111 college students, desirable learning outcomes were found in terms of cognitive load, motivation, and achievement. The multimedia pre-training group reported significantly less intrinsic cognitive load, higher motivation, and demonstrated higher transfer performance than the control and post-training groups. In this study, a statistical mediation model is also proposed to explain how cognitive load and motivation work in concert to foster learning from multimedia pre-training. This type of research goes beyond simple forms of "what works" to a deeper understanding of "how it works", thus enabling informed decisions for multimedia instructional design. Multimedia learning plays multiple roles in science education. Therefore, science learners would be some of the first to benefit from improving multimedia instructional design. Accordingly, complex scientific phenomena can be introduced to college students in a motivating, informative, and cognitively efficient learning environment.

Structural Biology of Proteins of the Multi-enzyme Assembly Human Pyruvate Dehydrogenase Complex

NASA Technical Reports Server (NTRS)

2003-01-01

Objectives and research challenges of this effort include: 1. Need to establish Human Pyruvate Dehydrogenase Complex protein crystals; 2. Need to test value of microgravity for improving crystal quality of Human Pyruvate Dehydrogenase Complex protein crystals; 3. Need to improve flight hardware in order to control and understand the effects of microgravity on crystallization of Human Pyruvate Dehydrogenase Complex proteins; 4. Need to integrate sets of national collaborations with the restricted and specific requirements of flight experiments; 5. Need to establish a highly controlled experiment in microgravity with a rigor not yet obtained; 6. Need to communicate both the rigor of microgravity experiments and the scientific value of results obtained from microgravity experiments to the national community; and 7. Need to advance the understanding of Human Pyruvate Dehydrogenase Complex structures so that scientific and commercial advance is identified for these proteins.

Emergence of Scale-Free Syntax Networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Valverde, Sergi; Solé, Ricard V.

The evolution of human language allowed the efficient propagation of nongenetic information, thus creating a new form of evolutionary change. Language development in children offers the opportunity of exploring the emergence of such complex communication system and provides a window to understanding the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The observed combinatorial patterns provide valuable data to understand the nature of the cognitive processes involved in the acquisition of syntax, introducing a new ingredient to understand the possible biological endowment of human beings which results in the emergence of complex language. We explore this problem by using a minimal, data-driven model that is able to capture several statistical traits, but some key features related to the emergence of syntactic complexity display important divergences.

New insights from monogenic diabetes for “common” type 2 diabetes

PubMed Central

Tallapragada, Divya Sri Priyanka; Bhaskar, Seema; Chandak, Giriraj R.

2015-01-01

Boundaries between monogenic and complex genetic diseases are becoming increasingly blurred, as a result of better understanding of phenotypes and their genetic determinants. This had a large impact on the way complex disease genetics is now being investigated. Starting with conventional approaches like familial linkage, positional cloning and candidate genes strategies, the scope of complex disease genetics has grown exponentially with scientific and technological advances in recent times. Despite identification of multiple loci harboring common and rare variants associated with complex diseases, interpreting and evaluating their functional role has proven to be difficult. Information from monogenic diseases, especially related to the intermediate traits associated with complex diseases comes handy. The significant overlap between traits and phenotypes of monogenic diseases with related complex diseases provides a platform to understand the disease biology better. In this review, we would discuss about one such complex disease, type 2 diabetes, which shares marked similarity of intermediate traits with different forms of monogenic diabetes. PMID:26300908

Synthetic biology in mammalian cells: Next generation research tools and therapeutics

PubMed Central

Lienert, Florian; Lohmueller, Jason J; Garg, Abhishek; Silver, Pamela A

2014-01-01

Recent progress in DNA manipulation and gene circuit engineering has greatly improved our ability to programme and probe mammalian cell behaviour. These advances have led to a new generation of synthetic biology research tools and potential therapeutic applications. Programmable DNA-binding domains and RNA regulators are leading to unprecedented control of gene expression and elucidation of gene function. Rebuilding complex biological circuits such as T cell receptor signalling in isolation from their natural context has deepened our understanding of network motifs and signalling pathways. Synthetic biology is also leading to innovative therapeutic interventions based on cell-based therapies, protein drugs, vaccines and gene therapies. PMID:24434884

The "What Is a System" Reflection Interview as a Knowledge Integration Activity for High School Students' Understanding of Complex Systems in Human Biology

ERIC Educational Resources Information Center

Tripto, Jaklin; Ben-Zvi Assaraf, Orit; Snapir, Zohar; Amit, Miriam

2016-01-01

This study examined the reflection interview as a tool for assessing and facilitating the use of "systems language" amongst 11th grade students who have recently completed their first year of high school biology. Eighty-three students composed two concept maps in the 10th grade--one at the beginning of the school year and one at its end.…

Drug Development and Biologics in Asthma. A New Era.

PubMed

Doyle, Ramona

2016-03-01

Considerable progress has been made toward developing targeted biological therapeutics for asthma, due in large part to a deeper understanding of asthma pathophysiology. This explosion of knowledge has revealed asthma to be a much more complex and heterogeneous entity than previously understood. The identification of particular asthma phenotypes with distinct pathophysiologic mechanisms has opened up a new era for patient populations not well served by current therapies, especially patients with severe asthma.

High-Molecular-Weight Proanthocyanidins in Foods: Overcoming Analytical Challenges in Pursuit of Novel Dietary Bioactive Components.

PubMed

Neilson, Andrew P; O'Keefe, Sean F; Bolling, Bradley W

2016-01-01

Proanthocyanidins (PACs) are an abundant but complex class of polyphenols found in foods and botanicals. PACs are polymeric flavanols with a variety of linkages and subunits. Connectivity and degree of polymerization (DP) determine PAC bioavailability and bioactivity. Current quantitative and qualitative methods may ignore a large percentage of dietary PACs. Subsequent correlations between intake and activity are hindered by a lack of understanding of the true PAC complexity in many foods. Additionally, estimates of dietary intakes are likely inaccurate, as nutrient databank values are largely based on standards from cocoa (monomers to decamers) and blueberries (mean DP of 36). Improved analytical methodologies are needed to increase our understanding of the biological roles of these complex compounds.

Introductory Biology Students' Use of Enhanced Answer Keys and Reflection Questions to Engage in Metacognition and Enhance Understanding.

PubMed

Sabel, Jaime L; Dauer, Joseph T; Forbes, Cory T

2017-01-01

Providing feedback to students as they learn to integrate individual concepts into complex systems is an important way to help them to develop robust understanding, but it is challenging in large, undergraduate classes for instructors to provide feedback that is frequent and directed enough to help individual students. Various scaffolds can be used to help students engage in self-regulated learning and generate internal feedback to improve their learning. This study examined the use of enhanced answer keys with added reflection questions and instruction as scaffolds for engaging undergraduate students in self-regulated learning within an introductory biology course. Study findings show that both the enhanced answer keys and reflection questions helped students to engage in metacognition and develop greater understanding of biological concepts. Further, students who received additional instruction on the use of the scaffolds changed how they used them and, by the end of the semester, were using the scaffolds in significantly different ways and showed significantly higher learning gains than students who did not receive the instruction. These findings provide evidence for the benefit of designing scaffolds within biology courses that will support students in engaging in metacognition and enhancing their understanding of biological concepts. © 2017 J. L. Sabel et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

PubMed

Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

2008-10-01

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

Systems Medicine: Sketching the Landscape.

PubMed

Kirschner, Marc

2016-01-01

To understand the meaning of the term Systems Medicine and to distinguish it from seemingly related other expressions currently in use, such as precision, personalized, -omics, or big data medicine, its underlying history and development into present time needs to be highlighted. Having this development in mind, it becomes evident that Systems Medicine is a genuine concept as well as a novel way of tackling the manifold complexity that occurs in nowadays clinical medicine-and not just a rebranding of what has previously been done in the past. So looking back it seems clear to many in the field that Systems Medicine has its origin in an integrative method to unravel biocomplexity, namely, Systems Biology. Here scientist by now gained useful experience that is on the verge toward implementation in clinical research and practice.Systems Medicine and Systems Biology have the same underlying theoretical principle in systems-based thinking-a methodology to understand complexity that can be traced back to ancient Greece. During the last decade, however, and due to a rapid methodological development in the life sciences and computing/IT technologies, Systems Biology has evolved from a scientific concept into an independent discipline most competent to tackle key questions of biocomplexity-with the potential to transform medicine and how it will be practiced in the future. To understand this process in more detail, the following section will thus give a short summary of the foundation of systems-based thinking and the different developmental stages including systems theory, the development of modern Systems Biology, and its transition into clinical practice. These are the components to pave the way toward Systems Medicine.

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph H.; Koch, Ina; Masoudi-Nejad, Ali

2014-01-01

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics. PMID:24822031

Searching and Mining Visually Observed Phenotypes of Maize Mutants

USDA-ARS?s Scientific Manuscript database

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most curr...

UNDERSTANDING THE ROLE OF OZONE STRESS IN ALTERING BELOWGROUND PROCESSES

EPA Science Inventory

Forested ecosystems are comprised of tremendous biological diversity and functional complexity both above and belowground. Soil ecosystems are known to contain thousands of species, with many more that have not yet been identified. Soil heterotrophic organisms depend on green p...

Applied statistics in agricultural, biological, and environmental sciences.

USDA-ARS?s Scientific Manuscript database

Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or s...

Single Cell Multiplex Protein Measurements through Rare Earth Element Immunolabeling, Laser Capture Microdissection and Inductively Coupled Mass Spectrometry.

PubMed

Liba, Amir; Wanagat, Jonathan

2014-11-01

Complex diseases such as heart disease, stroke, cancer, and aging are the primary causes of death in the US. These diseases cause heterogeneous conditions among cells, conditions that cannot be measured in tissue homogenates and require single cell approaches. Understanding protein levels within tissues is currently assayed using various molecular biology techniques (e.g., Western blots) that rely on milligram to gram quantities of tissue homogenates or immunofluorescent (IF) techniques that are limited by spectral overlap. Tissue homogenate studies lack references to tissue structure and mask signals from individual or rare cellular events. Novel techniques are required to bring protein measurement sensitivity to the single cell level and offer spatiotemporal resolution and scalability. We are developing a novel approach to protein quantification by exploiting the inherently low concentration of rare earth elements (REE) in biological systems. By coupling REE-antibody immunolabeling of cells with laser capture microdissection (LCM) and ICP-QQQ, we are achieving multiplexed protein measurement in histological sections of single cells. This approach will add to evolving single cell techniques and our ability to understand cellular heterogeneity in complex biological systems and diseases.

Patient Understanding of the Risks and Benefits of Biologic Therapies in Inflammatory Bowel Disease: Insights from a Large-scale Analysis of Social Media Platforms.

PubMed

Martinez, Bibiana; Dailey, Francis; Almario, Christopher V; Keller, Michelle S; Desai, Mansee; Dupuy, Taylor; Mosadeghi, Sasan; Whitman, Cynthia; Lasch, Karen; Ursos, Lyann; Spiegel, Brennan M R

2017-07-01

Few studies have examined inflammatory bowel disease (IBD) patients' knowledge and understanding of biologic therapies outside traditional surveys. Here, we used social media data to examine IBD patients' understanding of the risks and benefits associated with biologic therapies and how this affects decision-making. We collected posts from Twitter and e-forum discussions from >3000 social media sites posted between June 27, 2012 and June 27, 2015. Guided by natural language processing, we identified posts with specific IBD keywords that discussed the risks and/or benefits of biologics. We then manually coded the resulting posts and performed qualitative analysis using ATLAS.ti software. A hierarchical coding structure was developed based on the keyword list and relevant themes were identified through manual coding. We examined 1598 IBD-related posts, of which 452 (28.3%) centered on the risks and/or benefits of biologics. There were 5 main themes: negative experiences and concerns with biologics (n = 247; 54.6%), decision-making surrounding biologic use (n = 169; 37.4%), positive experiences with biologics (n = 168; 37.2%), information seeking from peers (n = 125; 27.7%), and cost (n = 38; 8.4%). Posts describing negative experiences primarily commented on side effects from biologics, concerns about potential side effects and increased cancer risk, and pregnancy safety concerns. Posts on decision-making focused on nonbiologic treatment options, hesitation to initiate biologics, and concerns about changing or discontinuing regimens. Social media reveals a wide range of themes governing patients' experience and choice with IBD biologics. The complexity of navigating their risk-benefit profiles suggests merit in creating online tailored decision tools to support IBD patients' decision-making with biologic therapies.

Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? What Really Constitutes the Study of "Systems Biology" and How Might Such an Approach Facilitate Vaccine Design.

PubMed

Germain, Ronald N

2017-10-16

A dichotomy exists in the field of vaccinology about the promise versus the hype associated with application of "systems biology" approaches to rational vaccine design. Some feel it is the only way to efficiently uncover currently unknown parameters controlling desired immune responses or discover what elements actually mediate these responses. Others feel that traditional experimental, often reductionist, methods for incrementally unraveling complex biology provide a more solid way forward, and that "systems" approaches are costly ways to collect data without gaining true insight. Here I argue that both views are inaccurate. This is largely because of confusion about what can be gained from classical experimentation versus statistical analysis of large data sets (bioinformatics) versus methods that quantitatively explain emergent properties of complex assemblies of biological components, with the latter reflecting what was previously called "physiology." Reductionist studies will remain essential for generating detailed insight into the functional attributes of specific elements of biological systems, but such analyses lack the power to provide a quantitative and predictive understanding of global system behavior. But by employing (1) large-scale screening methods for discovery of unknown components and connections in the immune system ( omics ), (2) statistical analysis of large data sets ( bioinformatics ), and (3) the capacity of quantitative computational methods to translate these individual components and connections into models of emergent behavior ( systems biology ), we will be able to better understand how the overall immune system functions and to determine with greater precision how to manipulate it to produce desired protective responses. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

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.

A brief introduction to mixed effects modelling and multi-model inference in ecology

PubMed Central

Donaldson, Lynda; Correa-Cano, Maria Eugenia; Goodwin, Cecily E.D.

2018-01-01

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions. PMID:29844961

A brief introduction to mixed effects modelling and multi-model inference in ecology.

PubMed

Harrison, Xavier A; Donaldson, Lynda; Correa-Cano, Maria Eugenia; Evans, Julian; Fisher, David N; Goodwin, Cecily E D; Robinson, Beth S; Hodgson, David J; Inger, Richard

2018-01-01

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

PubMed

Mishra, Bud; Daruwala, Raoul-Sam; Zhou, Yi; Ugel, Nadia; Policriti, Alberto; Antoniotti, Marco; Paxia, Salvatore; Rejali, Marc; Rudra, Archisman; Cherepinsky, Vera; Silver, Naomi; Casey, William; Piazza, Carla; Simeoni, Marta; Barbano, Paolo; Spivak, Marina; Feng, Jiawu; Gill, Ofer; Venkatesh, Mysore; Cheng, Fang; Sun, Bing; Ioniata, Iuliana; Anantharaman, Thomas; Hubbard, E Jane Albert; Pnueli, Amir; Harel, David; Chandru, Vijay; Hariharan, Ramesh; Wigler, Michael; Park, Frank; Lin, Shih-Chieh; Lazebnik, Yuri; Winkler, Franz; Cantor, Charles R; Carbone, Alessandra; Gromov, Mikhael

2003-01-01

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.

Clinical features and pathophysiology of Complex Regional Pain Syndrome – current state of the art

PubMed Central

Marinus, Johan; Moseley, G. Lorimer; Birklein, Frank; Baron, Ralf; Maihöfner, Christian; Kingery, Wade S.; van Hilten, Jacobus J.

2017-01-01

That a minor injury can trigger a complex regional pain syndrome (CRPS) - multiple system dysfunction, severe and often chronic pain and disability - has fascinated scientists and perplexed clinicians for decades. However, substantial advances across several medical disciplines have recently increased our understanding of CRPS. Compelling evidence implicates biological pathways that underlie aberrant inflammation, vasomotor dysfunction, and maladaptive neuroplasticity in the clinical features of CRPS. Collectively, the evidence points to CRPS being a multifactorial disorder that is associated with an aberrant host response to tissue injury. Varying susceptibility to perturbed regulation of any of the underlying biological pathways probably accounts for the clinical heterogeneity of CRPS. PMID:21683929

Complexity-aware simple modeling.

PubMed

Gómez-Schiavon, Mariana; El-Samad, Hana

2018-02-26

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spectroscopic exploration of interaction between PEG-functionalized Ag2S nanoparticles with bovine serum albumin

NASA Astrophysics Data System (ADS)

Prasanth, S.; RitheshRaj, D.; Vineeshkumar, T. V.; Sudarsanakumar, C.

2018-05-01

The introduction of nanoparticles into biological fluids often leads to the formation of biocorona over the surface of nanoparticles. For the effective use of nanoparticles in biological applications it is very essential to understand their interactions with proteins. Herein, we investigated the interactions of Poly ethylene glycol capped Ag2S nanoparticles with Bovine Serum Albumin by spectroscopic techniques. By the addition of Ag2S nanoparticles, a ground state complex is formed. The CD spectroscopy reveals that the secondary structure of BSA is altered by complexation with PEG-Ag2S nanoparticles, while the overall tertiary structure remains closer to that of native BSA.

“Gestaltomics”: Systems Biology Schemes for the Study of Neuropsychiatric Diseases

PubMed Central

Gutierrez Najera, Nora A.; Resendis-Antonio, Osbaldo; Nicolini, Humberto

2017-01-01

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed. PMID:28536537

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

PubMed Central

Miao, Yingbo; Zhang, Liangcai; Wang, Yang; Feng, Rennan; Yang, Lei; Zhang, Shihua; Jiang, Yongshuai; Liu, Guiyou

2014-01-01

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. PMID:25243143

A Tricky Trait: Applying the Fruits of the "Function Debate" in the Philosophy of Biology to the "Venom Debate" in the Science of Toxinology.

PubMed

Jackson, Timothy N W; Fry, Bryan G

2016-09-07

The "function debate" in the philosophy of biology and the "venom debate" in the science of toxinology are conceptually related. Venom systems are complex multifunctional traits that have evolved independently numerous times throughout the animal kingdom. No single concept of function, amongst those popularly defended, appears adequate to describe these systems in all their evolutionary contexts and extant variations. As such, a pluralistic view of function, previously defended by some philosophers of biology, is most appropriate. Venom systems, like many other functional traits, exist in nature as points on a continuum and the boundaries between "venomous" and "non-venomous" species may not always be clearly defined. This paper includes a brief overview of the concept of function, followed by in-depth discussion of its application to venom systems. A sound understanding of function may aid in moving the venom debate forward. Similarly, consideration of a complex functional trait such as venom may be of interest to philosophers of biology.

Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine.

PubMed

Mizeranschi, Alexandru; Groen, Derek; Borgdorff, Joris; Hoekstra, Alfons G; Chopard, Bastien; Dubitzky, Werner

2016-01-01

Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.

Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems.

PubMed

Bashor, Caleb J; Horwitz, Andrew A; Peisajovich, Sergio G; Lim, Wendell A

2010-01-01

The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.

Report of the matrix of biological knowledge workshop

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

Morowitz, H.J.; Smith, T.

1987-10-30

Current understanding of biology involves complex relationships rooted in enormous amounts of data. These data include entries from biochemistry, ecology, genetics, human and veterinary medicine, molecular structure studies, agriculture, embryology, systematics, and many other disciplines. The present wealth of biological data goes beyond past accumulations now include new understandings from molecular biology. Several important biological databases are currently being supported, and more are planned; however, major problems of interdatabase communication and management efficiency abound. Few scientists are currently capable of keeping up with this ever-increasing wealth of knowledge, let alone searching it efficiently for new or unsuspected links and importantmore » analogies. Yet this is what is required if the continued rapid generation of such data is to lead most effectively to the major conceptual, medical, and agricultural advances anticipated over the coming decades in the United States. The opportunity exists to combine the potential of modern computer science, database management, and artificial intelligence in a major effort to organize the vast wealth of biological and clinical data. The time is right because the amount of data is still manageable even in its current highly-fragmented form; important hardware and computer science tools have been greatly improved; and there have been recent fundamental advances in our comprehension of biology. This latter is particularly true at the molecular level where the information for nearly all higher structure and function is encoded. The organization of all biological experimental data coordinately within a structure incorporating our current understanding - the Matrix of Biological Knowledge - will provide the data and structure for the major advances foreseen in the years ahead.« less

Using Storyboarding to Model Gene Expression

ERIC Educational Resources Information Center

Korb, Michele; Colton, Shannon; Vogt, Gina

2015-01-01

Students often find it challenging to create images of complex, abstract biological processes. Using modified storyboards, which contain predrawn images, students can visualize the process and anchor ideas from activities, labs, and lectures. Storyboards are useful in assessing students' understanding of content in larger contexts. They enable…

Analysis of undergraduate students' conceptual models of a complex biological system across a diverse body of learners

NASA Astrophysics Data System (ADS)

Dirnbeck, Matthew R.

Biological systems pose a challenge both for learners and teachers because they are complex systems mediated by feedback loops; networks of cause-effect relationships; and non-linear, hierarchical, and emergent properties. Teachers and scientists routinely use models to communicate ideas about complex systems. Model-based pedagogies engage students in model construction as a means of practicing higher-order reasoning skills. One such modeling paradigm describes systems in terms of their structures, behaviors, and functions (SBF). The SBF framework is a simple modeling language that has been used to teach about complex biological systems. Here, we used student-generated SBF models to assess students' causal reasoning in the context of a novel biological problem on an exam. We compared students' performance on the modeling problem, their performance on a set of knowledge/comprehension questions, and their performance on a set of scientific reasoning questions. We found that students who performed well on knowledge and understanding questions also constructed more networked, higher quality models. Previous studies have shown that learners' mental maps increase in complexity with increased expertise. We wanted to investigate if biology students with varying levels of training in biology showed a similar pattern when constructing system models. In a pilot study, we administered the same modeling problem to two additional groups of students: 1) an animal physiology course for students pursuing a major in biology (n=37) and 2) an exercise physiology course for non-majors (n=27). We found that there was no significant difference in model organization across the three student populations, but there was a significant difference in the ability to represent function between the three populations. Between the three groups the non-majors had the lowest function scores, the introductory majors had the middle function scores, and the upper division majors had the highest function scores.

Minireview: Epigenetics of Obesity and Diabetes in Humans

PubMed Central

Slomko, Howard; Heo, Hye J.

2012-01-01

Understanding the determinants of human health and disease is overwhelmingly complex, particularly for common, late-onset, chronic disorders, such as obesity and diabetes. Elucidating the genetic and environmental factors that influence susceptibility to disruptions in energy homeostasis and metabolic regulation remain a challenge, and progress will entail the integration of multiple assessments of temporally dynamic environmental exposures in the context of each individual's genotype. To meet this challenge, researchers are increasingly exploring the epigenome, which is the malleable interface of gene-environment interactions. Epigenetic variation, whether innate or induced, contributes to variation in gene expression, the range of potential individual responses to internal and external cues, and risk for metabolic disease. Ultimately, advancement in our understanding of chronic disease susceptibility in humans will depend on refinement of exposure assessment tools and systems biology approaches to interpretation. In this review, we present recent progress in epigenetics of human obesity and diabetes, existing challenges, and the potential for new approaches to unravel the complex biology of metabolic dysregulation. PMID:22253427

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed Central

Bostrom, Mathias; O'Keefe, Regis

2009-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately. PMID:18612016

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed

Bostrom, Mathias; O'Keefe, Regis

2008-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately.

Integrating Epigenomics into the Understanding of Biomedical Insight.

PubMed

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

Integrating Epigenomics into the Understanding of Biomedical Insight

PubMed Central

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics. PMID:27980397

Minireview: Epigenetics of obesity and diabetes in humans.

PubMed

Slomko, Howard; Heo, Hye J; Einstein, Francine H

2012-03-01

Understanding the determinants of human health and disease is overwhelmingly complex, particularly for common, late-onset, chronic disorders, such as obesity and diabetes. Elucidating the genetic and environmental factors that influence susceptibility to disruptions in energy homeostasis and metabolic regulation remain a challenge, and progress will entail the integration of multiple assessments of temporally dynamic environmental exposures in the context of each individual's genotype. To meet this challenge, researchers are increasingly exploring the epigenome, which is the malleable interface of gene-environment interactions. Epigenetic variation, whether innate or induced, contributes to variation in gene expression, the range of potential individual responses to internal and external cues, and risk for metabolic disease. Ultimately, advancement in our understanding of chronic disease susceptibility in humans will depend on refinement of exposure assessment tools and systems biology approaches to interpretation. In this review, we present recent progress in epigenetics of human obesity and diabetes, existing challenges, and the potential for new approaches to unravel the complex biology of metabolic dysregulation.

Renal Tumor Anatomic Complexity: Clinical Implications for Urologists.

PubMed

Joshi, Shreyas S; Uzzo, Robert G

2017-05-01

Anatomic tumor complexity can be objectively measured and reported using nephrometry. Various scoring systems have been developed in an attempt to correlate tumor complexity with intraoperative and postoperative outcomes. Nephrometry may also predict tumor biology in a noninvasive, reproducible manner. Other scoring systems can help predict surgical complexity and the likelihood of complications, independent of tumor characteristics. The accumulated data in this new field provide provocative evidence that objectifying anatomic complexity can consolidate reporting mechanisms and improve metrics of comparisons. Further prospective validation is needed to understand the full descriptive and predictive ability of the various nephrometry scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Do endothelial cells dream of eclectic shape?

PubMed

Bentley, Katie; Philippides, Andrew; Ravasz Regan, Erzsébet

2014-04-28

Endothelial cells (ECs) exhibit dramatic plasticity of form at the single- and collective-cell level during new vessel growth, adult vascular homeostasis, and pathology. Understanding how, when, and why individual ECs coordinate decisions to change shape, in relation to the myriad of dynamic environmental signals, is key to understanding normal and pathological blood vessel behavior. However, this is a complex spatial and temporal problem. In this review we show that the multidisciplinary field of Adaptive Systems offers a refreshing perspective, common biological language, and straightforward toolkit that cell biologists can use to untangle the complexity of dynamic, morphogenetic systems. Copyright © 2014 Elsevier Inc. All rights reserved.

Evolving Concepts and Translational Relevance of Enteroendocrine Cell Biology.

PubMed

Drucker, Daniel J

2016-03-01

Classical enteroenteroendocrine cell (EEC) biology evolved historically from identification of scattered hormone-producing endocrine cells within the epithelial mucosa of the stomach, small and large intestine. Purification of functional EEC hormones from intestinal extracts, coupled with molecular cloning of cDNAs and genes expressed within EECs has greatly expanded the complexity of EEC endocrinology, with implications for understanding the contribution of EECs to disease pathophysiology. Pubmed searches identified manuscripts highlighting new concepts illuminating the molecular biology, classification and functional role(s) of EECs and their hormonal products. Molecular interrogation of EECs has been transformed over the past decade, raising multiple new questions that challenge historical concepts of EEC biology. Evidence for evolution of the EEC from a unihormonal cell type with classical endocrine actions, to a complex plurihormonal dynamic cell with pleiotropic interactive functional networks within the gastrointestinal mucosa is critically assessed. We discuss gaps in understanding how EECs sense and respond to nutrients, cytokines, toxins, pathogens, the microbiota, and the microbial metabolome, and highlight the expanding translational relevance of EECs in the pathophysiology and therapy of metabolic and inflammatory disorders. The EEC system represents the largest specialized endocrine network in human physiology, integrating environmental and nutrient cues, enabling neural and hormonal control of metabolic homeostasis. Updating EEC classification systems will enable more accurate comparative analyses of EEC subpopulations and endocrine networks in multiple regions of the gastrointestinal tract.

Encyrtid parasitoids of soft scale insects: biology, behavior, and their use in biological control.

PubMed

Kapranas, Apostolos; Tena, Alejandro

2015-01-07

Parasitoids of the hymenopterous family Encyrtidae are one of the most important groups of natural enemies of soft scale insects and have been used extensively in biological control. We summarize existing knowledge of the biology, ecology, and behavior of these parasitoids and how it relates to biological control. Soft scale stage/size and phenology are important determinants of host range and host utilization, which are key aspects in understanding how control by these parasitoids is exerted. Furthermore, the nutritional ecology of encyrtids and their physiological interactions with their hosts affect soft scale insect population dynamics. Lastly, the interactions among encyrtids, heteronomous parasitoids, and ants shape parasitoid species complexes and consequently have a direct impact on the biological control of soft scale insects.

Genetic Pedagogical Content Knowledge (PCK) Ability Profile of Prospective Biology Teacher

NASA Astrophysics Data System (ADS)

Purwianingsih, W.; Muthmainnah, E.; Hidayat, T.

2017-02-01

Genetics is one of the topics or subject matter in biology that are considered difficult. Student difficulties of understanding genetics, can be caused by lack of understanding this concept and the way of teachers teach. Pedagogical Content Knowledge (PCK) is a way to understand the complex relationships between teaching and content taught through the use of specific teaching approaches. The aims of study was to analyze genetic PCK ability profile of prospective biology teacher.13 student of sixth semester Biology education department who learned Kapita Selekta Biologi SMA course, participated in this study. PCK development was measured by CoRes (Content Representation). Before students fill CoRes, students are tested mastery genetic concepts through a multiple-choice test with three tier-test. Data was obtained from the prior CoRes and its revisions, as well as the mastery concept in pre and post test. Results showed that pre-test of genetic mastery concepts average on 55.4% (low category) and beginning of the writing CoRes, student get 43.2% (Pra PCK). After students get lecture and simulating learning, the post-test increased to 63.8% (sufficient category) and PCK revision is also increase 58.1% (growing PCK). It can be concluded that mastery of subject matter could affects the ability of genetic PCK.

Tagging and purifying proteins to teach molecular biology and advanced biochemistry.

PubMed

Roecklein-Canfield, Jennifer A; Lopilato, Jane

2004-11-01

Two distinct courses, "Molecular Biology" taught by the Biology Department and "Advanced Biochemistry" taught by the Chemistry Department, complement each other and, when taught in a coordinated and integrated way, can enhance student learning and understanding of complex material. "Molecular Biology" is a comprehensive lecture-based course with a 3-h laboratory once a week, while "Advanced Biochemistry" is a completely laboratory-based course with lecture fully integrated around independent student projects. Both courses emphasize and utilize cutting-edge technology. Teaching across departmental boundaries allows students access to faculty expertise and techniques rarely used at the undergraduate level, namely the tagging of proteins and their use in protein purification. Copyright © 2004 International Union of Biochemistry and Molecular Biology, Inc.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

PubMed Central

Gu, Shuo

2017-01-01

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed. PMID:28690664

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

PubMed

Gu, Shuo; Pei, Jianfeng

2017-01-01

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering.

PubMed

Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

2014-10-01

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. © 2014 American Society of Plant Biologists. All rights reserved.

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

PubMed Central

Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

2014-01-01

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. PMID:25344492

Proteomics for understanding miRNA biology

PubMed Central

Huang, Tai-Chung; Pinto, Sneha M.; Pandey, Akhilesh

2013-01-01

MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in posttranscriptional regulation of gene expression. Mature miRNAs associate with the RNA interference silencing complex to repress mRNA translation and/or degrade mRNA transcripts. Mass spectrometry-based proteomics has enabled identification of several core components of the canonical miRNA processing pathway and their posttranslational modifications which are pivotal in miRNA regulatory mechanisms. The use of quantitative proteomic strategies has also emerged as a key technique for experimental identification of miRNA targets by allowing direct determination of proteins whose levels are altered because of translational suppression. This review focuses on the role of proteomics and labeling strategies to understand miRNA biology. PMID:23125164

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

Visualizing protein interactions and dynamics: evolving a visual language for molecular animation.

PubMed

Jenkinson, Jodie; McGill, Gaël

2012-01-01

Undergraduate biology education provides students with a number of learning challenges. Subject areas that are particularly difficult to understand include protein conformational change and stability, diffusion and random molecular motion, and molecular crowding. In this study, we examined the relative effectiveness of three-dimensional visualization techniques for learning about protein conformation and molecular motion in association with a ligand-receptor binding event. Increasingly complex versions of the same binding event were depicted in each of four animated treatments. Students (n = 131) were recruited from the undergraduate biology program at University of Toronto, Mississauga. Visualization media were developed in the Center for Molecular and Cellular Dynamics at Harvard Medical School. Stem cell factor ligand and cKit receptor tyrosine kinase were used as a classical example of a ligand-induced receptor dimerization and activation event. Each group completed a pretest, viewed one of four variants of the animation, and completed a posttest and, at 2 wk following the assessment, a delayed posttest. Overall, the most complex animation was the most effective at fostering students' understanding of the events depicted. These results suggest that, in select learning contexts, increasingly complex representations may be more desirable for conveying the dynamic nature of cell binding events.

(PS)2: protein structure prediction server version 3.0.

PubMed

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens.

PubMed

Pan, Joshua; Meyers, Robin M; Michel, Brittany C; Mashtalir, Nazar; Sizemore, Ann E; Wells, Jonathan N; Cassel, Seth H; Vazquez, Francisca; Weir, Barbara A; Hahn, William C; Marsh, Joseph A; Tsherniak, Aviad; Kadoch, Cigall

2018-05-23

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks that recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone, and in combination with large-scale biophysical data, to enhance our understanding of mammalian protein complexes in normal and disease states. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm

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

Aderem, Alan; Adkins, Joshua N.; Ansong, Charles

The 20th century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and water borne illnesses are frequent, multi-drug resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past—including the intense focus on individual genes and proteins typical of molecular biology—have not been sufficient to address these challenges. The first decade of the 21st century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondinglymore » deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program we think that the time is at hand to redefine the pathogen-host research paradigm.« less

A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm

PubMed Central

Aderem, Alan; Adkins, Joshua N.; Ansong, Charles; Galagan, James; Kaiser, Shari; Korth, Marcus J.; Law, G. Lynn; McDermott, Jason G.; Proll, Sean C.; Rosenberger, Carrie; Schoolnik, Gary; Katze, Michael G.

2011-01-01

The twentieth century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and waterborne illnesses are frequent, multidrug-resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past—including the intense focus on individual genes and proteins typical of molecular biology—have not been sufficient to address these challenges. The first decade of the twenty-first century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondingly deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program, we think that the time is at hand to redefine the pathogen-host research paradigm. PMID:21285433

Systems biology in hepatology: approaches and applications.

PubMed

Mardinoglu, Adil; Boren, Jan; Smith, Ulf; Uhlen, Mathias; Nielsen, Jens

2018-06-01

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Interspecies Systems Biology Uncovers Metabolites Affecting C. elegans Gene Expression and Life History Traits

PubMed Central

Watson, Emma; MacNeil, Lesley T.; Ritter, Ashlyn D.; Yilmaz, L. Safak; Rosebrock, Adam P.; Caudy, Amy A.; Walhout, Albertha J. M.

2014-01-01

SUMMARY Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here we used an interspecies systems biology approach with Caenorhabditis elegans and two if its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal’s gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development and reduces fertility, but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology. PMID:24529378

Extended physics as a theoretical framework for systems biology?

PubMed

Miquel, Paul-Antoine

2011-08-01

In this essay we examine whether a theoretical and conceptual framework for systems biology could be built from the Bailly and Longo (2008, 2009) proposal. These authors aim to understand life as a coherent critical structure, and propose to develop an extended physical approach of evolution, as a diffusion of biomass in a space of complexity. Their attempt leads to a simple mathematical reconstruction of Gould's assumption (1989) concerning the bacterial world as a "left wall of least complexity" that we will examine. Extended physical systems are characterized by their constructive properties. Time is acting and new properties emerge by their history that can open the list of their initial properties. This conceptual and theoretical framework is nothing more than a philosophical assumption, but as such it provides a new and exciting approach concerning the evolution of life, and the transition between physics and biology. Copyright © 2011 Elsevier Ltd. All rights reserved.

The interactions of peripheral membrane proteins with biological membranes

DOE PAGES

Johs, Alexander; Whited, A. M.

2015-07-29

The interactions of peripheral proteins with membrane surfaces are critical to many biological processes, including signaling, recognition, membrane trafficking, cell division and cell structure. On a molecular level, peripheral membrane proteins can modulate lipid composition, membrane dynamics and protein-protein interactions. Biochemical and biophysical studies have shown that these interactions are in fact highly complex, dominated by several different types of interactions, and have an interdependent effect on both the protein and membrane. Here we examine three major mechanisms underlying the interactions between peripheral membrane proteins and membranes: electrostatic interactions, hydrophobic interactions, and fatty acid modification of proteins. While experimental approachesmore » continue to provide critical insights into specific interaction mechanisms, emerging bioinformatics resources and tools contribute to a systems-level picture of protein-lipid interactions. Through these recent advances, we begin to understand the pivotal role of protein-lipid interactions underlying complex biological functions at membrane interfaces.« less

Taking Systems Medicine to Heart.

PubMed

Trachana, Kalliopi; Bargaje, Rhishikesh; Glusman, Gustavo; Price, Nathan D; Huang, Sui; Hood, Leroy E

2018-04-27

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine. © 2018 American Heart Association, Inc.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Systems Biology Perspectives on Minimal and Simpler Cells

PubMed Central

Xavier, Joana C.; Patil, Kiran Raosaheb

2014-01-01

SUMMARY The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells. PMID:25184563

Micro/nanofabricated environments for synthetic biology.

PubMed

Collier, C Patrick; Simpson, Michael L

2011-08-01

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

EPA’s Children’s Environmental Health Roadmap: applying the 21st century vision to prenatal development (Teratology)

EPA Science Inventory

Understanding the complex relationships between environmental exposures and early life susceptibility in assessing the risk for adverse pregnancy outcomes requires advanced knowledge of biological systems. This broad research is one of several drivers for the Children’s Environme...

Proteogenomics | Office of Cancer Clinical Proteomics Research

Cancer.gov

Proteogenomics, or the integration of proteomics with genomics and transcriptomics, is an emerging approach that promises to advance basic, translational and clinical research.  By combining genomic and proteomic information, leading scientists are gaining new insights due to a more complete and unified understanding of complex biological processes.

Transcriptional pathway and de novo network-based approaches to effects-based monitoring in the Great Lakes

EPA Science Inventory

Transcriptomics provides unique solutions for understanding the impact of complex mixtures and their components on aquatic systems. Here we describe the application of transcriptomics analysis of in situ fathead minnow exposures for assessing biological impacts of wastewater trea...

Motivating Tomorrow's Biologists

ERIC Educational Resources Information Center

Musante, Susan

2012-01-01

The story of biology is far more complex and fascinating than straightforward facts or neatly labeled diagrams of structures and systems. Although exams can motivate students, the key to using these extrinsic motivators to increase student understanding lies in the way the assessments are designed and what they measure. Those involved in…

Information processing in bacteria: memory, computation, and statistical physics: a key issues review

NASA Astrophysics Data System (ADS)

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network—the main players (nodes) and their interactions (links)—in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.

Information processing in bacteria: memory, computation, and statistical physics: a key issues review.

PubMed

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network-the main players (nodes) and their interactions (links)-in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.

Human cumulative culture: a comparative perspective.

PubMed

Dean, Lewis G; Vale, Gill L; Laland, Kevin N; Flynn, Emma; Kendal, Rachel L

2014-05-01

Many animals exhibit social learning and behavioural traditions, but human culture exhibits unparalleled complexity and diversity, and is unambiguously cumulative in character. These similarities and differences have spawned a debate over whether animal traditions and human culture are reliant on homologous or analogous psychological processes. Human cumulative culture combines high-fidelity transmission of cultural knowledge with beneficial modifications to generate a 'ratcheting' in technological complexity, leading to the development of traits far more complex than one individual could invent alone. Claims have been made for cumulative culture in several species of animals, including chimpanzees, orangutans and New Caledonian crows, but these remain contentious. Whilst initial work on the topic of cumulative culture was largely theoretical, employing mathematical methods developed by population biologists, in recent years researchers from a wide range of disciplines, including psychology, biology, economics, biological anthropology, linguistics and archaeology, have turned their attention to the experimental investigation of cumulative culture. We review this literature, highlighting advances made in understanding the underlying processes of cumulative culture and emphasising areas of agreement and disagreement amongst investigators in separate fields. © 2013 The Authors. Biological Reviews © 2013 Cambridge Philosophical Society.

X-ray emission spectroscopy of biomimetic Mn coordination complexes

DOE PAGES

Jensen, Scott C.; Davis, Katherine M.; Sullivan,

2017-05-19

Understanding the function of Mn ions in biological and chemical redox catalysis requires precise knowledge of their electronic structure. X-ray emission spectroscopy (XES) is an emerging technique with a growing application to biological and biomimetic systems. Here, we report an improved, cost-effective spectrometer used to analyze two biomimetic coordination compounds, [Mn IV(OH) 2(Me 2EBC)] 2+ and [Mn IV(O)(OH)(Me 2EBC)] +, the second of which contains a key Mn IV=O structural fragment. Despite having the same formal oxidation state (Mn IV) and tetradentate ligands, XES spectra from these two compounds demonstrate different electronic structures. Experimental measurements and DFT calculations yield differentmore » localized spin densities for the two complexes resulting from Mn IV–OH conversion to Mn IV=O. The relevance of the observed spectroscopic changes is discussed for applications in analyzing complex biological systems such as photosystem II. In conclusion, a model of the S 3 intermediate state of photosystem II containing a Mn IV=O fragment is compared to recent time-resolved X-ray diffraction data of the same state.« less

X-ray Emission Spectroscopy of Biomimetic Mn Coordination Complexes.

PubMed

Jensen, Scott C; Davis, Katherine M; Sullivan, Brendan; Hartzler, Daniel A; Seidler, Gerald T; Casa, Diego M; Kasman, Elina; Colmer, Hannah E; Massie, Allyssa A; Jackson, Timothy A; Pushkar, Yulia

2017-06-15

Understanding the function of Mn ions in biological and chemical redox catalysis requires precise knowledge of their electronic structure. X-ray emission spectroscopy (XES) is an emerging technique with a growing application to biological and biomimetic systems. Here, we report an improved, cost-effective spectrometer used to analyze two biomimetic coordination compounds, [Mn IV (OH) 2 (Me 2 EBC)] 2+ and [Mn IV (O)(OH)(Me 2 EBC)] + , the second of which contains a key Mn IV ═O structural fragment. Despite having the same formal oxidation state (Mn IV ) and tetradentate ligands, XES spectra from these two compounds demonstrate different electronic structures. Experimental measurements and DFT calculations yield different localized spin densities for the two complexes resulting from Mn IV -OH conversion to Mn IV ═O. The relevance of the observed spectroscopic changes is discussed for applications in analyzing complex biological systems such as photosystem II. A model of the S 3 intermediate state of photosystem II containing a Mn IV ═O fragment is compared to recent time-resolved X-ray diffraction data of the same state.

Bone fracture healing in mechanobiological modeling: A review of principles and methods.

PubMed

Ghiasi, Mohammad S; Chen, Jason; Vaziri, Ashkan; Rodriguez, Edward K; Nazarian, Ara

2017-06-01

Bone fracture is a very common body injury. The healing process is physiologically complex, involving both biological and mechanical aspects. Following a fracture, cell migration, cell/tissue differentiation, tissue synthesis, and cytokine and growth factor release occur, regulated by the mechanical environment. Over the past decade, bone healing simulation and modeling has been employed to understand its details and mechanisms, to investigate specific clinical questions, and to design healing strategies. The goal of this effort is to review the history and the most recent work in bone healing simulations with an emphasis on both biological and mechanical properties. Therefore, we provide a brief review of the biology of bone fracture repair, followed by an outline of the key growth factors and mechanical factors influencing it. We then compare different methodologies of bone healing simulation, including conceptual modeling (qualitative modeling of bone healing to understand the general mechanisms), biological modeling (considering only the biological factors and processes), and mechanobiological modeling (considering both biological aspects and mechanical environment). Finally we evaluate different components and clinical applications of bone healing simulation such as mechanical stimuli, phases of bone healing, and angiogenesis.

The molecular biology of WHO grade I astrocytomas.

PubMed

Marko, Nicholas F; Weil, Robert J

2012-12-01

World Health Organization (WHO) grade I astrocytomas include pilocytic astrocytoma (PA) and subependymal giant cell astrocytoma (SEGA). As technologies in pharmacologic neo-adjuvant therapy continue to progress and as molecular characteristics are progressively recognized as potential markers of both clinically significant tumor subtypes and response to therapy, interest in the biology of these tumors has surged. An updated review of the current knowledge of the molecular biology of these tumors is needed. We conducted a Medline search to identify published literature discussing the molecular biology of grade I astrocytomas. We then summarized this literature and discuss it in a logical framework through which the complex biology of these tumors can be clearly understood. A comprehensive review of the molecular biology of WHO grade I astrocytomas is presented. The past several years have seen rapid progress in the level of understanding of PA in particular, but the molecular literature regarding both PA and SEGA remains nebulous, ambiguous, and occasionally contradictory. In this review we provide a comprehensive discussion of the current understanding of the chromosomal, genomic, and epigenomic features of both PA and SEGA and provide a logical framework in which these data can be more readily understood.

Information Literacy in Biology Education: An Example from an Advanced Cell Biology Course

PubMed Central

2005-01-01

Information literacy skills are critically important for the undergraduate biology student. The ability to find, understand, evaluate, and use information, whether from the scientific literature or from Web resources, is essential for a good understanding of a topic and for the conduct of research. A project in which students receive information literacy instruction and then proceed to select, update, and write about a current research topic in an upper-level cell biology course is described. Students research the chosen topic using paper and electronic resources, generate a list of relevant articles, prepare abstracts based on papers read, and, finally, prepare a “state-of-the-art” paper on the topic. This approach, which extends over most of one semester, has resulted in a number of well-researched and well-written papers that incorporate some of the latest research in cell biology. The steps in this project have also led to students who are prepared to address future projects on new and complex topics. The project is part of an undergraduate course in cell biology, but parts of the assignments can be modified to fit a variety of subject areas and levels. PMID:16341261

Materiomics: biological protein materials, from nano to macro.

PubMed

Cranford, Steven; Buehler, Markus J

2010-11-12

Materiomics is an emerging field of science that provides a basis for multiscale material system characterization, inspired in part by natural, for example, protein-based materials. Here we outline the scope and explain the motivation of the field of materiomics, as well as demonstrate the benefits of a materiomic approach in the understanding of biological and natural materials as well as in the design of de novo materials. We discuss recent studies that exemplify the impact of materiomics - discovering Nature's complexity through a materials science approach that merges concepts of material and structure throughout all scales and incorporates feedback loops that facilitate sensing and resulting structural changes at multiple scales. The development and application of materiomics is illustrated for the specific case of protein-based materials, which constitute the building blocks of a variety of biological systems such as tendon, bone, skin, spider silk, cells, and tissue, as well as natural composite material systems (a combination of protein-based and inorganic constituents) such as nacre and mollusk shells, and other natural multiscale systems such as cellulose-based plant and wood materials. An important trait of these materials is that they display distinctive hierarchical structures across multiple scales, where molecular details are exhibited in macroscale mechanical responses. Protein materials are intriguing examples of materials that balance multiple tasks, representing some of the most sustainable material solutions that integrate structure and function despite severe limitations in the quality and quantity of material building blocks. However, up until now, our attempts to analyze and replicate Nature's materials have been hindered by our lack of fundamental understanding of these materials' intricate hierarchical structures, scale-bridging mechanisms, and complex material components that bestow protein-based materials their unique properties. Recent advances in analytical tools and experimental methods allow a holistic view of such a hierarchical biological material system. The integration of these approaches and amalgamation of material properties at all scale levels to develop a complete description of a material system falls within the emerging field of materiomics. Materiomics is the result of the convergence of engineering and materials science with experimental and computational biology in the context of natural and synthetic materials. Through materiomics, fundamental advances in our understanding of structure-property-process relations of biological systems contribute to the mechanistic understanding of certain diseases and facilitate the development of novel biological, biologically inspired, and completely synthetic materials for applications in medicine (biomaterials), nanotechnology, and engineering.

Materiomics: biological protein materials, from nano to macro

PubMed Central

Cranford, Steven; Buehler, Markus J

2010-01-01

Materiomics is an emerging field of science that provides a basis for multiscale material system characterization, inspired in part by natural, for example, protein-based materials. Here we outline the scope and explain the motivation of the field of materiomics, as well as demonstrate the benefits of a materiomic approach in the understanding of biological and natural materials as well as in the design of de novo materials. We discuss recent studies that exemplify the impact of materiomics – discovering Nature’s complexity through a materials science approach that merges concepts of material and structure throughout all scales and incorporates feedback loops that facilitate sensing and resulting structural changes at multiple scales. The development and application of materiomics is illustrated for the specific case of protein-based materials, which constitute the building blocks of a variety of biological systems such as tendon, bone, skin, spider silk, cells, and tissue, as well as natural composite material systems (a combination of protein-based and inorganic constituents) such as nacre and mollusk shells, and other natural multiscale systems such as cellulose-based plant and wood materials. An important trait of these materials is that they display distinctive hierarchical structures across multiple scales, where molecular details are exhibited in macroscale mechanical responses. Protein materials are intriguing examples of materials that balance multiple tasks, representing some of the most sustainable material solutions that integrate structure and function despite severe limitations in the quality and quantity of material building blocks. However, up until now, our attempts to analyze and replicate Nature’s materials have been hindered by our lack of fundamental understanding of these materials’ intricate hierarchical structures, scale-bridging mechanisms, and complex material components that bestow protein-based materials their unique properties. Recent advances in analytical tools and experimental methods allow a holistic view of such a hierarchical biological material system. The integration of these approaches and amalgamation of material properties at all scale levels to develop a complete description of a material system falls within the emerging field of materiomics. Materiomics is the result of the convergence of engineering and materials science with experimental and computational biology in the context of natural and synthetic materials. Through materiomics, fundamental advances in our understanding of structure–property–process relations of biological systems contribute to the mechanistic understanding of certain diseases and facilitate the development of novel biological, biologically inspired, and completely synthetic materials for applications in medicine (biomaterials), nanotechnology, and engineering. PMID:24198478

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

PubMed

Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

2013-08-01

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

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

Hartmann, Anja, E-mail: hartmann@ipk-gatersleben.de; Schreiber, Falk; Martin-Luther-University Halle-Wittenberg, Halle

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the contextmore » of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM{sup 2} – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.« less

Hemojuvelin-hepcidin axis modeled and analyzed using Petri nets.

PubMed

Formanowicz, Dorota; Kozak, Adam; Głowacki, Tomasz; Radom, Marcin; Formanowicz, Piotr

2013-12-01

Systems biology approach to investigate biological phenomena seems to be very promising because it is capable to capture one of the fundamental properties of living organisms, i.e. their inherent complexity. It allows for analysis biological entities as complex systems of interacting objects. The first and necessary step of such an analysis is building a precise model of the studied biological system. This model is expressed in the language of some branch of mathematics, as for example, differential equations. During the last two decades the theory of Petri nets has appeared to be very well suited for building models of biological systems. The structure of these nets reflects the structure of interacting biological molecules and processes. Moreover, on one hand, Petri nets have intuitive graphical representation being very helpful in understanding the structure of the system and on the other hand, there is a lot of mathematical methods and software tools supporting an analysis of the properties of the nets. In this paper a Petri net based model of the hemojuvelin-hepcidin axis involved in the maintenance of the human body iron homeostasis is presented. The analysis based mainly on T-invariants of the model properties has been made and some biological conclusions have been drawn. Copyright © 2013 Elsevier Inc. All rights reserved.

The -omics Era- Toward a Systems-Level Understanding of Streptomyces

PubMed Central

Zhou, Zhan; Gu, Jianying; Du, Yi-Ling; Li, Yong-Quan; Wang, Yufeng

2011-01-01

Streptomyces is a group of soil bacteria of medicinal, economic, ecological, and industrial importance. It is renowned for its complex biology in gene regulation, antibiotic production, morphological differentiation, and stress response. In this review, we provide an overview of the recent advances in Streptomyces biology inspired by -omics based high throughput technologies. In this post-genomic era, vast amounts of data have been integrated to provide significant new insights into the fundamental mechanisms of system control and regulation dynamics of Streptomyces. PMID:22379394

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

PubMed

Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania

2009-10-15

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-01-01

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

Proteomic analysis in non-denaturing condition of the secretome reveals the presence of multienzyme complexes in Penicillium purpurogenum.

PubMed

Gonzalez-Vogel, Alvaro; Eyzaguirre, Jaime; Oleas, Gabriela; Callegari, Eduardo; Navarrete, Mario

2011-01-01

Proteins secreted by filamentous fungi play key roles in different aspects of their biology. The fungus Penicillium purpurogenum, used as a model organism, is able to degrade hemicelluloses and pectins by secreting a variety of enzymes to the culture medium. This work shows that these enzymes interact with each other to form high molecular weight, catalytically active complexes. By using a proteomics approach, we were able to identify several protein complexes in the secretome of this fungus. The expression and assembly of these complexes depend on the carbon source used and display molecular masses ranging from 300 to 700 kDa. These complexes are composed of a variety of enzymes, including arabinofuranosidases, acetyl xylan esterases, feruloyl esterases, β-glucosidases and xylanases. The protein-protein interactions in these multienzyme complexes were confirmed by coimmunoprecipitation assays. One of the complexes was purified from sugar beet pulp cultures and the subunits identified by tandem mass spectrometry. A better understanding of the biological significance of these kinds of interactions will help in the comprehension of the degradation mechanisms used by fungi and may be of special interest to the biotechnology industry.

Integrative mental health care: from theory to practice, Part 2.

PubMed

Lake, James

2008-01-01

Integrative approaches will lead to more accurate and different understandings of mental illness. Beneficial responses to complementary and alternative therapies provide important clues about the phenomenal nature of the human body in space-time and disparate biological, informational, and energetic factors associated with normal and abnormal psychological functioning. The conceptual framework of contemporary Western psychiatry includes multiple theoretical viewpoints, and there is no single best explanatory model of mental illness. Future theories of mental illness causation will not depend exclusively on empirical verification of strictly biological processes but will take into account both classically described biological processes and non-classical models, including complexity theory, resulting in more complete explanations of the characteristics and causes of symptoms and mechanisms of action that result in beneficial responses to treatments. Part 1 of this article examined the limitations of the theory and contemporary clinical methods employed in Western psychiatry and discussed implications of emerging paradigms in physics and the biological sciences for the future of psychiatry. In part 2, a practical methodology, for planning integrative assessment and treatment strategies in mental health care is proposed. Using this methodology the integrative management of moderate and severe psychiatric symptoms is reviewed in detail. As the conceptual framework of Western medicine evolves toward an increasingly integrative perspective, novel understanding of complex relationships between biological, informational, and energetic processes associated with normal psychological functioning and mental illness will lead to more effective integrative assessment and treatment strategies addressing the causes or meanings of symptoms at multiple hierarchic levels of body-brain-mind.

Integrative mental health care: from theory to practice, part 1.

PubMed

Lake, James

2007-01-01

Integrative approaches will lead to more accurate and different understandings of mental illness. Beneficial responses to complementary and alternative therapies provide important clues about the phenomenal nature of the human body in space-time and disparate biological, informational, and energetic factors associated with normal and abnormal psychological functioning. The conceptual framework of contemporary Western psychiatry includes multiple theoretical viewpoints, and there is no single best explanatory model of mental illness. Future theories of mental illness causation will not depend exclusively on empirical verification of strictly biological processes but will take into account both classically described biological processes and non-classical models, including complexity theory, resulting in more complete explanations of the characteristics and causes of symptoms and mechanisms of action that result in beneficial responses to treatments. Part 1 of this article examines the limitations of the theory and contemporary clinical methods employed in Western psychiatry and discusses implications of emerging paradigms in physics and the biological sciences for the future of psychiatry. In part 2, a practical methodology for planning integrative assessment and treatment strategies in mental health care is proposed. Using this methodology the integrative management of moderate and severe psychiatric symptoms is reviewed in detail. As the conceptual framework of Western medicine evolves toward an increasingly integrative perspective, novel understandings of complex relationships between biological, informational, and energetic processes associated with normal psychological functioning and mental illness will lead to more effective integrative assessment and treatment strategies addressing the causes or meanings of symptoms at multiple hierarchic levels of body-brain-mind.

Rapid self-assembly of complex biomolecular architectures during mussel byssus biofabrication

PubMed Central

Priemel, Tobias; Degtyar, Elena; Dean, Mason N.; Harrington, Matthew J.

2017-01-01

Protein-based biogenic materials provide important inspiration for the development of high-performance polymers. The fibrous mussel byssus, for instance, exhibits exceptional wet adhesion, abrasion resistance, toughness and self-healing capacity–properties that arise from an intricate hierarchical organization formed in minutes from a fluid secretion of over 10 different protein precursors. However, a poor understanding of this dynamic biofabrication process has hindered effective translation of byssus design principles into synthetic materials. Here, we explore mussel byssus assembly in Mytilus edulis using a synergistic combination of histological staining and confocal Raman microspectroscopy, enabling in situ tracking of specific proteins during induced thread formation from soluble precursors to solid fibres. Our findings reveal critical insights into this complex biological manufacturing process, showing that protein precursors spontaneously self-assemble into complex architectures, while maturation proceeds in subsequent regulated steps. Beyond their biological importance, these findings may guide development of advanced materials with biomedical and industrial relevance. PMID:28262668

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

Horowitz, Scott; Salmon, Loïc; Koldewey, Philipp

We present that challenges in determining the structures of heterogeneous and dynamic protein complexes have greatly hampered past efforts to obtain a mechanistic understanding of many important biological processes. One such process is chaperone-assisted protein folding. Obtaining structural ensembles of chaperone–substrate complexes would ultimately reveal how chaperones help proteins fold into their native state. To address this problem, we devised a new structural biology approach based on X-ray crystallography, termed residual electron and anomalous density (READ). READ enabled us to visualize even sparsely populated conformations of the substrate protein immunity protein 7 (Im7) in complex with the Escherichia coli chaperonemore » Spy, and to capture a series of snapshots depicting the various folding states of Im7 bound to Spy. The ensemble shows that Spy-associated Im7 samples conformations ranging from unfolded to partially folded to native-like states and reveals how a substrate can explore its folding landscape while being bound to a chaperone.« less

Biological removal of NOx from flue gas.

PubMed

Kumaraswamy, R; Muyzer, G; Kuenen, J G; Loosdrecht, M C M

2004-01-01

BioDeNOx is a novel integrated physico-chemical and biological process for the removal of nitrogen oxides (NOx) from flue gas. Due to the high temperature of flue gas the process is performed at a temperature between 50-55 degrees C. Flue gas containing CO2, O2, SO2 and NOx, is purged through Fe(II)EDTA2- containing liquid. The Fe(II)EDTA2- complex effectively binds the NOx; the bound NOx is converted into N2 in a complex reaction sequence. In this paper an overview of the potential microbial reactions in the BioDeNOx process is discussed. It is evident that though the process looks simple, due to the large number of parallel potential reactions and serial microbial conversions, it is much more complex. There is a need for a detailed investigation in order to properly understand and optimise the process.

Iron, ferritin, and nutrition.

PubMed

Theil, Elizabeth C

2004-01-01

Ferritin, a major form of endogenous iron in food legumes such as soybeans, is a novel and natural alternative for iron supplementation strategies where effectiveness is limited by acceptability, cost, or undesirable side effects. A member of the nonheme iron group of dietary iron sources, ferritin is a complex with Fe3+ iron in a mineral (thousands of iron atoms inside a protein cage) protected from complexation. Ferritin illustrates the wide range of chemical and biological properties among nonheme iron sources. The wide range of nonheme iron receptors matched to the structure of the iron complexes that occurs in microorganisms may, by analogy, exist in humans. An understanding of the chemistry and biology of each type of dietary iron source (ferritin, heme, Fe2+ ion, etc.), and of the interactions dependent on food sources, genes, and gender, is required to design diets that will eradicate global iron deficiency in the twenty-first century.

State of research: environmental pathways and food chain transfer.

PubMed Central

Vaughan, B E

1984-01-01

Data on the chemistry of biologically active components of petroleum, synthetic fuel oils, certain metal elements and pesticides provide valuable generic information needed for predicting the long-term fate of buried waste constituents and their likelihood of entering food chains. Components of such complex mixtures partition between solid and solution phases, influencing their mobility, volatility and susceptibility to microbial transformation. Estimating health hazards from indirect exposures to organic chemicals involves an ecosystem's approach to understanding the unique behavior of complex mixtures. Metabolism by microbial organisms fundamentally alters these complex mixtures as they move through food chains. Pathway modeling of organic chemicals must consider the nature and magnitude of food chain transfers to predict biological risk where metabolites may become more toxic than the parent compound. To obtain predictions, major areas are identified where data acquisition is essential to extend our radiological modeling experience to the field of organic chemical contamination. PMID:6428875

Synthesis of Polycyclic Natural Products

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

Nguyen, Tuan Hoang

With the continuous advancements in molecular biology and modern medicine, organic synthesis has become vital to the support and extension of those discoveries. The isolations of new natural products allow for the understanding of their biological activities and therapeutic value. Organic synthesis is employed to aid in the determination of the relationship between structure and function of these natural products. The development of synthetic methodologies in the course of total syntheses is imperative for the expansion of this highly interdisciplinary field of science. In addition to the practical applications of total syntheses, the structural complexity of natural products represents amore » worthwhile challenge in itself. The pursuit of concise and efficient syntheses of complex molecules is both gratifying and enjoyable.« less

Mass Spectrometry: A Technique of Many Faces

PubMed Central

Olshina, Maya A.; Sharon, Michal

2016-01-01

Protein complexes form the critical foundation for a wide range of biological process, however understanding the intricate details of their activities is often challenging. In this review we describe how mass spectrometry plays a key role in the analysis of protein assemblies and the cellular pathways which they are involved in. Specifically, we discuss how the versatility of mass spectrometric approaches provides unprecedented information on multiple levels. We demonstrate this on the ubiquitin-proteasome proteolytic pathway, a process that is responsible for protein turnover. We follow the various steps of this degradation route and illustrate the different mass spectrometry workflows that were applied for elucidating molecular information. Overall, this review aims to stimulate the integrated use of multiple mass spectrometry approaches for analyzing complex biological systems. PMID:28100928

Biosimilars--global issues, national solutions.

PubMed

Knezevic, Ivana; Griffiths, Elwyn

2011-09-01

Biotechnology derived medicinal products are presently the best characterized biologicals with considerable production and clinical experience, and have revolutionized the treatment of some of the most difficult-to-treat diseases, prolonging and improving the quality of life and patient care. They are also currently one of the fastest growing segments of the pharmaceutical industry market. The critical challenge that the biopharmaceutical industry is facing is the expiry of patents for the first generation of biopharmaceuticals, mainly recombinant DNA derived products, such as interferons, growth hormone and erythropoetin. The question that immediately arose was how should such copies of the originator products be licensed, bearing in mind that they are highly complex biological molecules produced by equally complex biological production processes with their inherent problem of biological variability. Copying biologicals is much more complex than copying small molecules and the critical issue was how to handle the licensing of products if relying in part on data from an innovator product. Since 2004 there has been considerable international consultation on how to deal with biosimilars and biological copy products. This has led to a better understanding of the challenges in the regulatory evaluation of the quality, safety and efficacy of "biosimilars", to the exchange of information between regulators, as well as to the identification of key issues. The aim of this article is to provide a brief overview of the scientific and regulatory challenges faced in developing and evaluating similar biotherapeutic products for global use. It is intended as an introduction to the series of articles in this special issue of Biologicals devoted to similar biotherapeutic products. Copyright © 2011. Published by Elsevier Ltd.

NCI-CPTAC DREAM Proteogenomics Challenge (Registration Now Open) | Office of Cancer Clinical Proteomics Research

Cancer.gov

Proteogenomics, integration of proteomics, genomics, and transcriptomics, is an emerging approach that promises to advance basic, translational and clinical research.  By combining genomic and proteomic information, leading scientists are gaining new insights due to a more complete and unified understanding of complex biological processes.

Sowing the Seeds of Creativity

ERIC Educational Resources Information Center

Briten, Elizabeth

2006-01-01

The exciting world of plants may be something of a mystery to many children, and the often-dry content of a curriculum taught indoors inhibits real understanding of many complex biological processes. Moving outdoors opens up an unexplored world and presents rich opportunities for imaginative learning. The "Life processes and living…

2011 Strategic Plan for Autism Spectrum Disorder Research

ERIC Educational Resources Information Center

Interagency Autism Coordinating Committee, 2011

2011-01-01

Autism spectrum disorder (ASD) affects an estimated 1% of children in the United States and yet many fundamental questions about the biology of ASD, potential risk factors, effective treatments and interventions, and impacts throughout life remain unanswered. Important advances have been made in understanding the complexity of ASD, but additional…

The DNA Triangle and Its Application to Learning Meiosis

ERIC Educational Resources Information Center

Wright, L. Kate; Catavero, Christina M.; Newman, Dina L.

2017-01-01

Although instruction on meiosis is repeated many times during the undergraduate curriculum, many students show poor comprehension even as upper-level biology majors. We propose that the difficulty lies in the complexity of understanding DNA, which we explain through a new model, the DNA triangle. The "DNA triangle" integrates three…

Biology Diagrams: Tools To Think With.

ERIC Educational Resources Information Center

Kindfield, Ann C. H.

Subcellular processes like meiosis are frequently problematic for learners because they are complex and, except for the extent that they can be observed under a light microscope, occur outside of our direct experience. More detailed characterization of what underlies various degrees of student understanding of a process is required to more fully…

Book Review: "The Rhizosphere: Biochemistry and Organic Substances at the Soil-Plant Interface, Second Edition"

USDA-ARS?s Scientific Manuscript database

The complexity of the biological, chemical, and physical interactions occurring in the volume of soil surrounding the root of a growing plant dictates that a multidisciplinary approach must be taken to improve our understanding of this rhizosphere. Hence, "The Rhizosphere: Biochemistry and Organic S...

Molecular Thermodynamics for Cell Biology as Taught with Boxes

ERIC Educational Resources Information Center

Mayorga, Luis S.; Lopez, Maria Jose; Becker, Wayne M.

2012-01-01

Thermodynamic principles are basic to an understanding of the complex fluxes of energy and information required to keep cells alive. These microscopic machines are nonequilibrium systems at the micron scale that are maintained in pseudo-steady-state conditions by very sophisticated processes. Therefore, several nonstandard concepts need to be…

What Does Culture Have to Do with Teaching Science?

ERIC Educational Resources Information Center

Madden, Lauren; Joshi, Arti

2013-01-01

In nearly every elementary school, plants are an important part of the science curriculum. Understanding basic ideas about plants prepares children to study more complicated scientific concepts including cell biology, genetics and heredity, complex ecosystem interactions, and evolution. It is especially important that teachers of children at the…

Introducing the Hero Complex and the Mythic Iconic Pathway of Problem Gambling

ERIC Educational Resources Information Center

Nixon, Gary; Solowoniuk, Jason

2009-01-01

Early research into the motivations behind problem gambling reflected separate paradigms of thought splitting our understanding of the gambler into divergent categories. However, over the past 25 years, problem gambling is now best understood to arise from biological, environmental, social, and psychological processes, and is now encapsulated…

CARBON AND NITROGEN ALLOCATION MODEL FOR THE SEAGRASS THALASSIA TESTUDUNUM IN LOWER LAGUNA MADRE

EPA Science Inventory

Inverse modeling methods are a powerful tool for understanding complex physiological relationships between seagrasses and their environment. The power of the method is a result of using ranges of data in a system of constraints to describe the biological system, in this case, t...

About the Alliance | Division of Cancer Prevention

Cancer.gov

Objectives The major objective of the Alliance is to discover and develop molecular markers for early detection of cancer by conducting innovative, translational research in the field of complex carbohydrates. An important key to biomarker discovery is to understand the biological mechanisms by which changes in glycosylation promote cancer progression. Taking this

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

Towards Biological Inspiration in the Development of Complex Systems

NASA Technical Reports Server (NTRS)

Hinchey, Michael G.; Sterritt, Roy

2006-01-01

Greater understanding of biology in modem times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in "biologically inspired" systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be inspired by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems.

A systems biology approach to studying Tai Chi, physiological complexity and healthy aging: design and rationale of a pragmatic randomized controlled trial.

PubMed

Wayne, Peter M; Manor, Brad; Novak, Vera; Costa, Madelena D; Hausdorff, Jeffrey M; Goldberger, Ary L; Ahn, Andrew C; Yeh, Gloria Y; Peng, C-K; Lough, Matthew; Davis, Roger B; Quilty, Mary T; Lipsitz, Lewis A

2013-01-01

Aging is typically associated with progressive multi-system impairment that leads to decreased physical and cognitive function and reduced adaptability to stress. Due to its capacity to characterize complex dynamics within and between physiological systems, the emerging field of complex systems biology and its array of quantitative tools show great promise for improving our understanding of aging, monitoring senescence, and providing biomarkers for evaluating novel interventions, including promising mind-body exercises, that treat age-related disease and promote healthy aging. An ongoing, two-arm randomized clinical trial is evaluating the potential of Tai Chi mind-body exercise to attenuate age-related loss of complexity. A total of 60 Tai Chi-naïve healthy older adults (aged 50-79) are being randomized to either six months of Tai Chi training (n=30), or to a waitlist control receiving unaltered usual medical care (n=30). Our primary outcomes are complexity-based measures of heart rate, standing postural sway and gait stride interval dynamics assessed at 3 and 6months. Multiscale entropy and detrended fluctuation analysis are used as entropy- and fractal-based measures of complexity, respectively. Secondary outcomes include measures of physical and psychological function and tests of physiological adaptability also assessed at 3 and 6months. Results of this study may lead to novel biomarkers that help us monitor and understand the physiological processes of aging and explore the potential benefits of Tai Chi and related mind-body exercises for healthy aging. Copyright © 2012 Elsevier Inc. All rights reserved.

SWI/SNF Chromatin-remodeling Factors: Multiscale Analyses and Diverse Functions*

PubMed Central

Euskirchen, Ghia; Auerbach, Raymond K.; Snyder, Michael

2012-01-01

Chromatin-remodeling enzymes play essential roles in many biological processes, including gene expression, DNA replication and repair, and cell division. Although one such complex, SWI/SNF, has been extensively studied, new discoveries are still being made. Here, we review SWI/SNF biochemistry; highlight recent genomic and proteomic advances; and address the role of SWI/SNF in human diseases, including cancer and viral infections. These studies have greatly increased our understanding of complex nuclear processes. PMID:22952240

Unraveling human complexity and disease with systems biology and personalized medicine

PubMed Central

Naylor, Stephen; Chen, Jake Y

2010-01-01

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1–10 million proteins (including antibodies) and the 2000–3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level. PMID:20577569

Peroxisystem: harnessing systems cell biology to study peroxisomes.

PubMed

Schuldiner, Maya; Zalckvar, Einat

2015-04-01

In recent years, high-throughput experimentation with quantitative analysis and modelling of cells, recently dubbed systems cell biology, has been harnessed to study the organisation and dynamics of simple biological systems. Here, we suggest that the peroxisome, a fascinating dynamic organelle, can be used as a good candidate for studying a complete biological system. We discuss several aspects of peroxisomes that can be studied using high-throughput systematic approaches and be integrated into a predictive model. Such approaches can be used in the future to study and understand how a more complex biological system, like a cell and maybe even ultimately a whole organism, works. © 2015 Société Française des Microscopies and Société de Biologie Cellulaire de France. Published by John Wiley & Sons Ltd.

Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network

PubMed Central

Wang, Bingbo; Gao, Lin; Zhang, Qingfang; Li, Aimin; Deng, Yue; Guo, Xingli

2015-01-01

Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies. PMID:26284649

Sealable femtoliter chamber arrays for cell-free biology

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

Retterer, Scott T.; Fowlkes, Jason Davidson; Collier, Charles Patrick

Cell-free systems provide a flexible platform for probing specific networks of biological reactions isolated from the complex resource sharing (e.g. global gene expression, cell division) encountered within living cells. However, such systems, used in conventional macro-scale bulk reactors, often fail to exhibit the dynamic behaviors and efficiencies characteristic of their living micro-scale counterparts. Understanding the impact of internal cell structure and scale on reaction dynamics is crucial to understanding complex gene networks. Here we report a microfabricated device that confines cell-free reactions in cellular scale volumes while allowing flexible characterization of the enclosed molecular system. This multilayered poly(dimethylsiloxane) (PDMS) devicemore » contains femtoliter-scale reaction chambers on an elastomeric membrane which can be actuated (open and closed). When actuated, the chambers confine Cell-Free Protein Synthesis (CFPS) reactions expressing a fluorescent protein, allowing for the visualization of the reaction kinetics over time using time-lapse fluorescent microscopy. Lastly, we demonstrate how this device may be used to measure the noise structure of CFPS reactions in a manner that is directly analogous to those used to characterize cellular systems, thereby enabling the use of noise biology techniques to characterize CFPS gene circuits and their interactions with the cell-free environment.« less

Sealable femtoliter chamber arrays for cell-free biology

DOE PAGES

Retterer, Scott T.; Fowlkes, Jason Davidson; Collier, Charles Patrick; ...

2015-03-11

Cell-free systems provide a flexible platform for probing specific networks of biological reactions isolated from the complex resource sharing (e.g. global gene expression, cell division) encountered within living cells. However, such systems, used in conventional macro-scale bulk reactors, often fail to exhibit the dynamic behaviors and efficiencies characteristic of their living micro-scale counterparts. Understanding the impact of internal cell structure and scale on reaction dynamics is crucial to understanding complex gene networks. Here we report a microfabricated device that confines cell-free reactions in cellular scale volumes while allowing flexible characterization of the enclosed molecular system. This multilayered poly(dimethylsiloxane) (PDMS) devicemore » contains femtoliter-scale reaction chambers on an elastomeric membrane which can be actuated (open and closed). When actuated, the chambers confine Cell-Free Protein Synthesis (CFPS) reactions expressing a fluorescent protein, allowing for the visualization of the reaction kinetics over time using time-lapse fluorescent microscopy. Lastly, we demonstrate how this device may be used to measure the noise structure of CFPS reactions in a manner that is directly analogous to those used to characterize cellular systems, thereby enabling the use of noise biology techniques to characterize CFPS gene circuits and their interactions with the cell-free environment.« less

A morphospace for synthetic organs and organoids: the possible and the actual.

PubMed

Ollé-Vila, Aina; Duran-Nebreda, Salva; Conde-Pueyo, Núria; Montañez, Raúl; Solé, Ricard

2016-04-18

Efforts in evolutionary developmental biology have shed light on how organs are developed and why evolution has selected some structures instead of others. These advances in the understanding of organogenesis along with the most recent techniques of organotypic cultures, tissue bioprinting and synthetic biology provide the tools to hack the physical and genetic constraints in organ development, thus opening new avenues for research in the form of completely designed or merely altered settings. Here we propose a unifying framework that connects the concept of morphospace (i.e. the space of possible structures) with synthetic biology and tissue engineering. We aim for a synthesis that incorporates our understanding of both evolutionary and architectural constraints and can be used as a guide for exploring alternative design principles to build artificial organs and organoids. We present a three-dimensional morphospace incorporating three key features associated to organ and organoid complexity. The axes of this space include the degree of complexity introduced by developmental mechanisms required to build the structure, its potential to store and react to information and the underlying physical state. We suggest that a large fraction of this space is empty, and that the void might offer clues for alternative ways of designing and even inventing new organs.

Hierarchical coordinate systems for understanding complexity and its evolution, with applications to genetic regulatory networks.

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

PubMed Central

Galatzer-Levy, Isaac R.; Ruggles, Kelly; Chen, Zhe

2017-01-01

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals. PMID:29527592

Tutorial: Electroporation of cells in complex materials and tissue

NASA Astrophysics Data System (ADS)

Rems, L.; Miklavčič, D.

2016-05-01

Electroporation is being successfully used in biology, medicine, food processing, and biotechnology, and in some environmental applications. Recent applications also include in addition to classical electroporation, where cells are exposed to micro- or milliseconds long pulses, exposures to extremely short nanosecond pulses, i.e., high-frequency electroporation. Electric pulses are applied to cells in different structural configurations ranging from suspended cells to cells in tissues. Understanding electroporation of cells in tissues and other complex environments is a key to its successful use and optimization in various applications. Thus, explanation will be provided theoretically/numerically with relation to experimental observations by scaling our understanding of electroporation from the molecular level of the cell membrane up to the tissue level.

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.

In vitro studies of actin filament and network dynamics

PubMed Central

Mullins, R Dyche; Hansen, Scott D

2013-01-01

Now that many genomes have been sequenced, a central concern of cell biology is to understand how the proteins they encode work together to create living matter. In vitro studies form an essential part of this program because understanding cellular functions of biological molecules often requires isolating them and reconstituting their activities. In particular, many elements of the actin cytoskeleton were first discovered by biochemical methods and their cellular functions deduced from in vitro experiments. We highlight recent advances that have come from in vitro studies, beginning with studies of actin filaments, and ending with multi-component reconstitutions of complex actin-based processes, including force-generation and cell spreading. We describe both scientific results and the technical innovations that made them possible. PMID:23267766

Bacteriophage lambda: early pioneer and still relevant

PubMed Central

Casjens, Sherwood R.; Hendrix, Roger W.

2015-01-01

Molecular genetic research on bacteriophage lambda carried out during its golden age from the mid 1950's to mid 1980's was critically important in the attainment of our current understanding of the sophisticated and complex mechanisms by which the expression of genes is controlled, of DNA virus assembly and of the molecular nature of lysogeny. The development of molecular cloning techniques, ironically instigated largely by phage lambda researchers, allowed many phage workers to switch their efforts to other biological systems. Nonetheless, since that time the ongoing study of lambda and its relatives have continued to give important new insights. In this review we give some relevant early history and describe recent developments in understanding the molecular biology of lambda's life cycle. PMID:25742714

Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

NASA Technical Reports Server (NTRS)

Szallasi, Zoltan; Liang, Shoudan

2000-01-01

In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

A Systems Biology Approach to Iron Metabolism

PubMed Central

Chifman, J.; Laubenbacher, R.; Torti, S.V.

2015-01-01

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying causes. PMID:25480643

Biological Networks for Cancer Candidate Biomarkers Discovery

PubMed Central

Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

2016-01-01

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

Biological Control of Appetite: A Daunting Complexity

PubMed Central

MacLean, Paul S.; Blundell, John E.; Mennella, Julie A.; Batterham, Rachel L.

2017-01-01

Objective This review summarizes a portion of the discussions of an NIH Workshop (Bethesda, MD, 2015) entitled, “Self-Regulation of Appetite, It's Complicated,” which focused on the biological aspects of appetite regulation. Methods Here we summarize the key biological inputs of appetite regulation and their implications for body weight regulation. Results These discussions offer an update of the long-held, rigid perspective of an “adipocentric” biological control, taking a broader view that also includes important inputs from the digestive tract, from lean mass, and from the chemical sensory systems underlying taste and smell. We are only beginning to understand how these biological systems are integrated and how this integrated input influences appetite and food eating behaviors. The relevance of these biological inputs was discussed primarily in the context of obesity and the problem of weight regain, touching on topics related to the biological predisposition for obesity and the impact that obesity treatments (dieting, exercise, bariatric surgery, etc.) might have on appetite and weight loss maintenance. Finally, we consider a common theme that pervaded the workshop discussions, which was individual variability. Conclusions It is this individual variability in the predisposition for obesity and in the biological response to weight loss that makes the biological component of appetite regulation so complicated. When this individual biological variability is placed in the context of the diverse environmental and behavioral pressures that also influence food eating behaviors, it is easy to appreciate the daunting complexities that arise with the self-regulation of appetite. PMID:28229538

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

PubMed Central

Kawakami, Eiryo; Singh, Vivek K; Matsubara, Kazuko; Ishii, Takashi; Matsuoka, Yukiko; Hase, Takeshi; Kulkarni, Priya; Siddiqui, Kenaz; Kodilkar, Janhavi; Danve, Nitisha; Subramanian, Indhupriya; Katoh, Manami; Shimizu-Yoshida, Yuki; Ghosh, Samik; Jere, Abhay; Kitano, Hiroaki

2016-01-01

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker’s or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow–tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow–tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems. PMID:28725465

Network Analysis: A Novel Approach to Understand Suicidal Behaviour

PubMed Central

de Beurs, Derek

2017-01-01

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behaviour. Suicidal behaviour is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behaviour, but we do not yet understand how their interaction increases the risk for suicidal behaviour. A new approach called network analysis can help us better understand this process as it allows us to visualize and quantify the complex association between many different symptoms or risk factors. A network analysis of data containing information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

Understanding the complexities of Salmonella-host crosstalk as revealed by in vivo model organisms.

PubMed

Verma, Smriti; Srikanth, Chittur V

2015-07-01

Foodborne infections caused by non-typhoidal Salmonellae, such as Salmonella enterica serovar Typhimurium (ST), pose a major challenge in the developed and developing world. With constant rise of drug-resistant strains, understanding the epidemiology, microbiology, pathogenesis and host-pathogen interactions biology is a mandatory requirement to enable health systems to be ready to combat these illnesses. Patient data from hospitals, at least from some parts of the world, have aided in epidemiological understanding of ST-mediated disease. Most of the other aspects connected to Salmonella-host crosstalk have come from model systems that offer convenience, genetic tractability and low maintenance costs that make them extremely valuable tools. Complex model systems such as the bovine model have helped in understanding key virulence factors needed for infection. Simple systems such as fruit flies and Caenorhabditis elegans have aided in identification of novel virulence factors, host pathways and mechanistic details of interactions. Some of the path-breaking concepts of the field have come from mice model of ST colitis, which allows genetic manipulations as well as high degree of similarity to human counterpart. Together, they are invaluable for correlating in vitro findings of ST-induced disease progression in vivo. The current review is a compilation of various advances of ST-host interactions at cellular and molecular levels that has come from investigations involving model organisms. © 2015 International Union of Biochemistry and Molecular Biology.

An efficient grid layout algorithm for biological networks utilizing various biological attributes

PubMed Central

Kojima, Kaname; Nagasaki, Masao; Jeong, Euna; Kato, Mitsuru; Miyano, Satoru

2007-01-01

Background Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i) the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii) from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii) it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. Results We propose a new grid layout algorithm. To address problem (i), we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii), we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii), we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates. Conclusion Layouts of the new grid layout algorithm are compared with that of the previous algorithm and human layout in an endothelial cell model, three times as large as the apoptosis model. The total cost of the result from the new grid layout algorithm is similar to that of the human layout. In addition, its convergence time is drastically reduced (40% reduction). PMID:17338825

The Evolving Contribution of Mass Spectrometry to Integrative Structural Biology

NASA Astrophysics Data System (ADS)

Faini, Marco; Stengel, Florian; Aebersold, Ruedi

2016-06-01

Protein complexes are key catalysts and regulators for the majority of cellular processes. Unveiling their assembly and structure is essential to understanding their function and mechanism of action. Although conventional structural techniques such as X-ray crystallography and NMR have solved the structure of important protein complexes, they cannot consistently deal with dynamic and heterogeneous assemblies, limiting their applications to small scale experiments. A novel methodological paradigm, integrative structural biology, aims at overcoming such limitations by combining complementary data sources into a comprehensive structural model. Recent applications have shown that a range of mass spectrometry (MS) techniques are able to generate interaction and spatial restraints (cross-linking MS) information on native complexes or to study the stoichiometry and connectivity of entire assemblies (native MS) rapidly, reliably, and from small amounts of substrate. Although these techniques by themselves do not solve structures, they do provide invaluable structural information and are thus ideally suited to contribute to integrative modeling efforts. The group of Brian Chait has made seminal contributions in the use of mass spectrometric techniques to study protein complexes. In this perspective, we honor the contributions of the Chait group and discuss concepts and milestones of integrative structural biology. We also review recent examples of integration of structural MS techniques with an emphasis on cross-linking MS. We then speculate on future MS applications that would unravel the dynamic nature of protein complexes upon diverse cellular states.

Mapping complex traits as a dynamic system

PubMed Central

Sun, Lidan; Wu, Rongling

2017-01-01

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a “system” in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states. PMID:25772476

Biologically-inspired approaches for self-organization, adaptation, and collaboration of heterogeneous autonomous systems

NASA Astrophysics Data System (ADS)

Steinberg, Marc

2011-06-01

This paper presents a selective survey of theoretical and experimental progress in the development of biologicallyinspired approaches for complex surveillance and reconnaissance problems with multiple, heterogeneous autonomous systems. The focus is on approaches that may address ISR problems that can quickly become mathematically intractable or otherwise impractical to implement using traditional optimization techniques as the size and complexity of the problem is increased. These problems require dealing with complex spatiotemporal objectives and constraints at a variety of levels from motion planning to task allocation. There is also a need to ensure solutions are reliable and robust to uncertainty and communications limitations. First, the paper will provide a short introduction to the current state of relevant biological research as relates to collective animal behavior. Second, the paper will describe research on largely decentralized, reactive, or swarm approaches that have been inspired by biological phenomena such as schools of fish, flocks of birds, ant colonies, and insect swarms. Next, the paper will discuss approaches towards more complex organizational and cooperative mechanisms in team and coalition behaviors in order to provide mission coverage of large, complex areas. Relevant team behavior may be derived from recent advances in understanding of the social and cooperative behaviors used for collaboration by tens of animals with higher-level cognitive abilities such as mammals and birds. Finally, the paper will briefly discuss challenges involved in user interaction with these types of systems.

A systematic analysis of the Braitenberg vehicle 2b for point-like stimulus sources.

PubMed

Rañó, Iñaki

2012-09-01

Braitenberg vehicles have been used experimentally for decades in robotics with limited empirical understanding. This paper presents the first mathematical model of the vehicle 2b, displaying so-called aggression behaviour, and analyses the possible trajectories for point-like smooth stimulus sources. This sensory-motor steering control mechanism is used to implement biologically grounded target approach, target-seeking or obstacle-avoidance behaviour. However, the analysis of the resulting model reveals that complex and unexpected trajectories can result even for point-like stimuli. We also prove how the implementation of the controller and the vehicle morphology interact to affect the behaviour of the vehicle. This work provides a better understanding of Braitenberg vehicle 2b, explains experimental results and paves the way for a formally grounded application on robotics as well as for a new way of understanding target seeking in biology.

Perspectives of a systems biology of the brain: the big data conundrum understanding psychiatric diseases.

PubMed

Mewes, H W

2013-05-01

Psychiatric diseases provoke human tragedies. Asocial behaviour, mood imbalance, uncontrolled affect, and cognitive malfunction are the price for the biological and social complexity of neurobiology. To understand the etiology and to influence the onset and progress of mental diseases remains of upmost importance, but despite the much improved care for the patients, more then 100 years of research have not succeeded to understand the basic disease mechanisms and enabling rationale treatment. With the advent of the genome based technologies, much hope has been created to join the various dimension of -omics data to uncover the secrets of mental illness. Big Data as generated by -omics do not come with explanations. In this essay, I will discuss the inherent, not well understood methodological foundations and problems that seriously obstacle in striving for a quick success and may render lucky strikes impossible. © Georg Thieme Verlag KG Stuttgart · New York.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

PubMed Central

Pathak, Rajesh Kumar; Gupta, Sanjay Mohan; Gaur, Vikram Singh; Pandey, Dinesh

2015-01-01

Abstract In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes. PMID:26484978

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why.

PubMed

Stéphanou, Angélique; Fanchon, Eric; Innominato, Pasquale F; Ballesta, Annabelle

2018-05-09

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

A Tricky Trait: Applying the Fruits of the “Function Debate” in the Philosophy of Biology to the “Venom Debate” in the Science of Toxinology

PubMed Central

Jackson, Timothy N. W.; Fry, Bryan G.

2016-01-01

The “function debate” in the philosophy of biology and the “venom debate” in the science of toxinology are conceptually related. Venom systems are complex multifunctional traits that have evolved independently numerous times throughout the animal kingdom. No single concept of function, amongst those popularly defended, appears adequate to describe these systems in all their evolutionary contexts and extant variations. As such, a pluralistic view of function, previously defended by some philosophers of biology, is most appropriate. Venom systems, like many other functional traits, exist in nature as points on a continuum and the boundaries between “venomous” and “non-venomous” species may not always be clearly defined. This paper includes a brief overview of the concept of function, followed by in-depth discussion of its application to venom systems. A sound understanding of function may aid in moving the venom debate forward. Similarly, consideration of a complex functional trait such as venom may be of interest to philosophers of biology. PMID:27618098

From Molecules to Life: Quantifying the Complexity of Chemical and Biological Systems in the Universe.

PubMed

Böttcher, Thomas

2018-01-01

Life is a complex phenomenon and much research has been devoted to both understanding its origins from prebiotic chemistry and discovering life beyond Earth. Yet, it has remained elusive how to quantify this complexity and how to compare chemical and biological units on one common scale. Here, a mathematical description of molecular complexity was applied allowing to quantitatively assess complexity of chemical structures. This in combination with the orthogonal measure of information complexity resulted in a two-dimensional complexity space ranging over the entire spectrum from molecules to organisms. Entities with a certain level of information complexity directly require a functionally complex mechanism for their production or replication and are hence indicative for life-like systems. In order to describe entities combining molecular and information complexity, the term biogenic unit was introduced. Exemplified biogenic unit complexities were calculated for ribozymes, protein enzymes, multimeric protein complexes, and even an entire virus particle. Complexities of prokaryotic and eukaryotic cells, as well as multicellular organisms, were estimated. Thereby distinct evolutionary stages in complexity space were identified. The here developed approach to compare the complexity of biogenic units allows for the first time to address the gradual characteristics of prebiotic and life-like systems without the need for a definition of life. This operational concept may guide our search for life in the Universe, and it may direct the investigations of prebiotic trajectories that lead towards the evolution of complexity at the origins of life.

The Physics of Life and Quantum Complex Matter: A Case of Cross-Fertilization

PubMed Central

Poccia, Nicola; Bianconi, Antonio

2011-01-01

Progress in the science of complexity, from the Big Bang to the coming of humankind, from chemistry and biology to geosciences and medicine, and from materials engineering to energy sciences, is leading to a shift of paradigm in the physical sciences. The focus is on the understanding of the non-equilibrium process in fine tuned systems. Quantum complex materials such as high temperature superconductors and living matter are both non-equilibrium and fine tuned systems. These topics have been subbjects of scientific discussion in the Rome Symposium on the “Quantum Physics of Living Matter”. PMID:26791661

Systems biology perspectives on minimal and simpler cells.

PubMed

Xavier, Joana C; Patil, Kiran Raosaheb; Rocha, Isabel

2014-09-01

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Bayesian approach to MSD-based analysis of particle motion in live cells.

PubMed

Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

2012-08-08

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Convolving engineering and medical pedagogies for training of tomorrow's health care professionals.

PubMed

Lee, Raphael C

2013-03-01

Several fundamental benefits justify why biomedical engineering and medicine should form a more convergent alliance, especially for the training of tomorrow's physicians and biomedical engineers. Herein, we review the rationale underlying the benefits. Biological discovery has advanced beyond the era of molecular biology well into today's era of molecular systems biology, which focuses on understanding the rules that govern the behavior of complex living systems. This has important medical implications. To realize cost-effective personalized medicine, it is necessary to translate the advances in molecular systems biology to higher levels of biological organization (organ, system, and organismal levels) and then to develop new medical therapeutics based on simulation and medical informatics analysis. Higher education in biological and medical sciences must adapt to a new set of training objectives. This will involve a shifting away from reductionist problem solving toward more integrative, continuum, and predictive modeling approaches which traditionally have been more associated with engineering science. Future biomedical engineers and MDs must be able to predict clinical response to therapeutic intervention. Medical education will involve engineering pedagogies, wherein basic governing rules of complex system behavior and skill sets in manipulating these systems to achieve a practical desired outcome are taught. Similarly, graduate biomedical engineering programs will include more practical exposure to clinical problem solving.

Systems biology: An emerging strategy for discovering novel pathogenetic mechanisms that promote cardiovascular disease.

PubMed

Maron, Bradley A; Leopold, Jane A

2016-09-30

Reductionist theory proposes that analyzing complex systems according to their most fundamental components is required for problem resolution, and has served as the cornerstone of scientific methodology for more than four centuries. However, technological gains in the current scientific era now allow for the generation of large datasets that profile the proteomic, genomic, and metabolomic signatures of biological systems across a range of conditions. The accessibility of data on such a vast scale has, in turn, highlighted the limitations of reductionism, which is not conducive to analyses that consider multiple and contemporaneous interactions between intermediates within a pathway or across constructs. Systems biology has emerged as an alternative approach to analyze complex biological systems. This methodology is based on the generation of scale-free networks and, thus, provides a quantitative assessment of relationships between multiple intermediates, such as protein-protein interactions, within and between pathways of interest. In this way, systems biology is well positioned to identify novel targets implicated in the pathogenesis or treatment of diseases. In this review, the historical root and fundamental basis of systems biology, as well as the potential applications of this methodology are discussed with particular emphasis on integration of these concepts to further understanding of cardiovascular disorders such as coronary artery disease and pulmonary hypertension.

Complex Systems

PubMed Central

Goldberger, Ary L.

2006-01-01

Physiologic systems in health and disease display an extraordinary range of temporal behaviors and structural patterns that defy understanding based on linear constructs, reductionist strategies, and classical homeostasis. Application of concepts and computational tools derived from the contemporary study of complex systems, including nonlinear dynamics, fractals and “chaos theory,” is having an increasing impact on biology and medicine. This presentation provides a brief overview of an emerging area of biomedical research, including recent applications to cardiopulmonary medicine and chronic obstructive lung disease. PMID:16921107

Multiscale agent-based cancer modeling.

PubMed

Zhang, Le; Wang, Zhihui; Sagotsky, Jonathan A; Deisboeck, Thomas S

2009-04-01

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.

Towards systemic theories in biological psychiatry.

PubMed

Bender, W; Albus, M; Möller, H-J; Tretter, F

2006-02-01

Although still rather controversial, empirical data on the neurobiology of schizophrenia have reached a degree of complexity that makes it hard to obtain a coherent picture of the malfunctions of the brain in schizophrenia. Theoretical neuropsychiatry should therefore use the tools of theoretical sciences like cybernetics, informatics, computational neuroscience or systems science. The methodology of systems science permits the modeling of complex dynamic nonlinear systems. Such procedures might help us to understand brain functions and the disorders and actions of psychiatric drugs better.

Visualizing Protein Interactions and Dynamics: Evolving a Visual Language for Molecular Animation

PubMed Central

Jenkinson, Jodie; McGill, Gaël

2012-01-01

Undergraduate biology education provides students with a number of learning challenges. Subject areas that are particularly difficult to understand include protein conformational change and stability, diffusion and random molecular motion, and molecular crowding. In this study, we examined the relative effectiveness of three-dimensional visualization techniques for learning about protein conformation and molecular motion in association with a ligand–receptor binding event. Increasingly complex versions of the same binding event were depicted in each of four animated treatments. Students (n = 131) were recruited from the undergraduate biology program at University of Toronto, Mississauga. Visualization media were developed in the Center for Molecular and Cellular Dynamics at Harvard Medical School. Stem cell factor ligand and cKit receptor tyrosine kinase were used as a classical example of a ligand-induced receptor dimerization and activation event. Each group completed a pretest, viewed one of four variants of the animation, and completed a posttest and, at 2 wk following the assessment, a delayed posttest. Overall, the most complex animation was the most effective at fostering students' understanding of the events depicted. These results suggest that, in select learning contexts, increasingly complex representations may be more desirable for conveying the dynamic nature of cell binding events. PMID:22383622

Complex and biofluids: From Maxwell to nowadays

NASA Astrophysics Data System (ADS)

Misbah, Chaouqi

2009-11-01

Complex fluids are the rule in biology and in many industrial applications. Typical examples are blood, cartilage, and polymer solutions. Unlike water (as well as domestic oils, soft clear drinks, and so on), the law(s) describing the behavior of complex fluids are not yet fully established. The complexity arises from strong coupling between microscopic scales (like the motion of a red blood cell in the case of blood, or a polymer molecule for a polymer solution) and the global scale of the flow (say at the scale of a blood artery, or a channel in laboratory experiments). In this issue entitled Complex and Biofluids a large panel of experimental and theoretical problems of complex fluids is exposed. The topics range from dilute polymer solutions, food products, to biology (blood flow, cell and tissue mechanics). One of the earliest model put forward as an attempt to describe a complex fluid was suggested a long time ago by James Clerk Maxwell (in 1867). Other famous scientists, like Einstein (in 1906), and Taylor (in 1932) have made important contributions to the field, but the topic of complex fluids still continues to pose a formidable challenge to science. This field has known during the past decade an unbelievable upsurge of interest in many branches of science (physics, mechanics, chemistry, biology, medical science, mathematics, and so on). Understanding complex fluids is viewed as one of the biggest challenge of the present century. This synthesis will provide a simple introduction to the topic, summarize the main contribution of this issue, and list major open questions in this field. To cite this article: C. Misbah, C. R. Physique 10 (2009).

Understanding the Thermodynamics of Biological Order

ERIC Educational Resources Information Center

Peterson, Jacob

2012-01-01

By growth in size and complexity (i.e., changing from more probable to less probable states), plants and animals appear to defy the second law of thermodynamics. The usual explanation describes the input of nutrient and sunlight energy into open thermodynamic systems. However, energy input alone does not address the ability to organize and create…

Essential Properties of Language, or, Why Language Is Not a Code

ERIC Educational Resources Information Center

Kravchenko, Alexander V.

2007-01-01

Despite a strong tradition of viewing "coded equivalence" as the underlying principle of linguistic semiotics, it lacks the power needed to understand and explain language as an empirical phenomenon characterized by complex dynamics. Applying the biology of cognition to the nature of the human cognitive/linguistic capacity as rooted in the…

Evaluation of Software for Introducing Protein Structure: Visualization and Simulation

ERIC Educational Resources Information Center

White, Brian; Kahriman, Azmin; Luberice, Lois; Idleh, Farhia

2010-01-01

Communicating an understanding of the forces and factors that determine a protein's structure is an important goal of many biology and biochemistry courses at a variety of levels. Many educators use computer software that allows visualization of these complex molecules for this purpose. Although visualization is in wide use and has been associated…

The Historical Development of Vaccine Technology: Exploring the Relationship between Science and Technology

ERIC Educational Resources Information Center

Lee, Yeung Chung; Kwok, Ping Wai

2017-01-01

This paper examines the feasibility of using historical case studies to contextualise the learning of the nature of science and technology in a biology lesson. Through exploring the historical development of vaccine technology, students were expected to understand the complexity of the relationships between technology and science beyond the…

The Role of Socioscientific Issues in Biology Teaching: From the Perspective of Teachers

ERIC Educational Resources Information Center

Tidemand, Sofie; Nielsen, Jan Alexis

2017-01-01

Previous research has documented that students who engage with socioscientific issues can acquire some of the complex competences and skills typically related to scientific literacy. But an emerging field of research on science teachers' understanding and use of socioscientific issues, has documented that a range of challenges hinders the uptake…

The Tentative Nature of Scientific Knowledge: Why Should We Teach More about Diabetes Mellitus?

ERIC Educational Resources Information Center

Biermann, Carol A.

1993-01-01

Almost 70 years of scientific research leaves many unanswered questions concerning diabetes mellitus. This disease can be viewed as an illustration of the complexity of biological systems. Textbooks stress normal rather than abnormal physiology and rarely share the difficulties encountered in understanding those abnormal conditions (PR)

Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

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

Winkler, David A., E-mail: dave.winkler@csiro.au

2016-05-15

Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based,more » have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods. - Highlights: • Nanomaterials regulators need good information to make good decisions. • Nanomaterials and their interactions with biology are very complex. • Computational methods use existing data to predict properties of new nanomaterials. • Statistical, data driven modelling methods have been successfully applied to this task. • Much more must be learnt before robust toolkits will be widely usable by regulators.« less

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.

Meaningful crosstalk between biologists and physical scientists is essential for modern biology to progress.

PubMed

Dev, Sukhendu B

2009-01-01

The advances in biological sciences have been phenomenal since the structure of DNA was decoded, especially if one considers the input from physical sciences, not only in terms of analytical tools, but also understanding and solving some of the key problems in biology. In this article, I trace briefly the history of this transition, from physical sciences to biology, and argue that progress in modern biology can be accelerated if there is far more meaningful crosstalk between the biologists and the physical scientists, simply because biology has become far more complex and interdisciplinary, and the need for such crosstalk cannot be overemphasized. Without a concerted effort in this area progress will be hindered, and the two camps will continue to work on their own, using their own specialized language, thus making communication highly ineffective. I support my argument giving a vast array of examples and also quoting leading authorities.

A complexity basis for phenomenology: How information states at criticality offer a new approach to understanding experience of self, being and time.

PubMed

Hankey, Alex

2015-12-01

In the late 19th century Husserl studied our internal sense of time passing, maintaining that its deep connections into experience represent prima facie evidence for it as the basis for all investigations in the sciences: Phenomenology was born. Merleau-Ponty focused on perception pointing out that any theory of experience must accord with established aspects of biology i.e. be embodied. Recent analyses suggest that theories of experience require non-reductive, integrative information, together with a specific property connecting them to experience. Here we elucidate a new class of information states with just such properties found at the loci of control of complex biological systems, including nervous systems. Complexity biology concerns states satisfying self-organized criticality. Such states are located at critical instabilities, commonly observed in biological systems, and thought to maximize information diversity and processing, and hence to optimize regulation. Major results for biology follow: why organisms have unusually low entropies; and why they are not merely mechanical. Criticality states form singular self-observing systems, which reduce wave packets by processes of perfect self-observation associated with feedback gain g = 1. Analysis of their information properties leads to identification of a new kind of information state with high levels of internal coherence, and feedback loops integrated into their structure. The major idea presented here is that the integrated feedback loops are responsible for our 'sense of self', and also the feeling of continuity in our sense of time passing. Long-range internal correlations guarantee a unique kind of non-reductive, integrative information structure enabling such states to naturally support phenomenal experience. Being founded in complexity biology, they are 'embodied'; they also fulfill the statement that 'The self is a process', a singular process. High internal correlations and René Thom-style catastrophes support non-digital forms of information, gestalt cognition, and information transfer via quantum teleportation. Criticality in complexity biology can 'embody' cognitive states supporting gestalts, and phenomenology's senses of 'self,' time passing, existence and being. Copyright © 2015. Published by Elsevier Ltd.

Topological Principles of Control in Dynamical Networks

NASA Astrophysics Data System (ADS)

Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

Introduction to the Special Issue: Beyond traits: integrating behaviour into plant ecology and biology.

PubMed

Cahill, James F

2015-10-26

The way that plants are conceptualized in the context of ecological understanding is changing. In one direction, a reductionist school is pulling plants apart into a list of measured 'traits', from which ecological function and outcomes of species interactions may be inferred. This special issue offers an alternative, and more holistic, view: that the ecological functions performed by a plant will be a consequence not only of their complement of traits but also of the ways in which their component parts are used in response to environmental and social conditions. This is the realm of behavioural ecology, a field that has greatly advanced our understanding of animal biology, ecology and evolution. Included in this special issue are 10 articles focussing not on the tried and true metaphor that plant growth is similar to animal movement, but instead on how application of principles from animal behaviour can improve our ability to understand plant biology and ecology. The goals are not to draw false parallels, nor to anthropomorphize plant biology, but instead to demonstrate how existing and robust theory based on fundamental principles can provide novel understanding for plants. Key to this approach is the recognition that behaviour and intelligence are not the same. Many organisms display complex behaviours despite a lack of cognition (as it is traditionally understood) or any hint of a nervous system. The applicability of behavioural concepts to plants is further enhanced with the realization that all organisms face the same harsh forces of natural selection in the context of finding resources, mates and coping with neighbours. As these ecological realities are often highly variable in space and time, it is not surprising that all organisms-even plants-exhibit complex behaviours to handle this variability. The articles included here address diverse topics in behavioural ecology, as applied to plants: general conceptual understanding, plant nutrient foraging, root-root interactions, and using and helping others. As a group, the articles in this special issue demonstrate how plant ecological understanding can be enhanced through incorporation of behavioural ideas and set the stage for future research in the emerging discipline of plant behavioural ecology. Published by Oxford University Press on behalf of the Annals of Botany Company.

Editorial overview: Omics

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

Barran, Perdita; Baker, Erin

The great complexity of biological systems and their environment poses similarly vast challenges for accurate analytical evaluations of their identity, structure and quantity. Post genomic science, has predicted much regarding the static populations of biological systems, but a further challenge for analysis is to test the accuracy of these predictions, as well as provide a better representation of the transient nature of the molecules of life. Accurate measurements of biological systems have wide applications for biological, forensic, biotechnological and healthcare fields. Therefore, the holy grail is to find a technique which can identify and quantify biological molecules with high throughput,more » sensitivity and robustness, as well evaluate molecular structure(s) in order to understand how the specific molecules interact and function. While wrapping all of these characteristics into one platform may sound difficult, ion mobility spectrometry (IMS) is addressing all of these challenges. Over the last decade, the number of analytical studies utilizing IMS for the evaluation of complex biological and environmental samples has greatly increased. In most cases IMS is coupled with mass spectrometry (IM-MS), but even alone IMS provides the unique capability of rapidly assessing a molecule’s structure, which can be extremely difficult with other techniques. The robustness of the IMS measurement is bourne out by its widespread use in security, environmental and military applications. The multidimensional IM-MS measurements however have been proven to be ever more powerful, as applied to complex mixtures as they enable the evaluation of both the structure and mass of every molecular component in a sample during a single measurement, without the need for continual reference calibration.« less

Enhancement of COPD biological networks using a web-based collaboration interface

PubMed Central

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C.; Schlage, Walter K.; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V.; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C.; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks. PMID:25767696

Enhancement of COPD biological networks using a web-based collaboration interface.

PubMed

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C; Schlage, Walter K; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

Insights From Genomics Into Spatial and Temporal Variation in Batrachochytrium dendrobatidis.

PubMed

Byrne, A Q; Voyles, J; Rios-Sotelo, G; Rosenblum, E B

2016-01-01

Advances in genetics and genomics have provided new tools for the study of emerging infectious diseases. Researchers can now move quickly from simple hypotheses to complex explanations for pathogen origin, spread, and mechanisms of virulence. Here we focus on the application of genomics to understanding the biology of the fungal pathogen Batrachochytrium dendrobatidis (Bd), a novel and deadly pathogen of amphibians. We provide a brief history of the system, then focus on key insights into Bd variation garnered from genomics approaches, and finally, highlight new frontiers for future discoveries. Genomic tools have revealed unexpected complexity and variation in the Bd system suggesting that the history and biology of emerging pathogens may not be as simple as they initially seem. Copyright © 2016 Elsevier Inc. All rights reserved.

Reasoning from non-stationarity

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.; van Wijngaarden, Willem J.; Castelo, Robert

2002-11-01

Complex real-world (biological) systems often exhibit intrinsically non-stationary behaviour of their temporal characteristics. We discuss local measures of scaling which can capture and reveal changes in a system's behaviour. Such measures offer increased insight into a system's behaviour and are superior to global, spectral characteristics like the multifractal spectrum. They are, however, often inadequate for fully understanding and modelling the phenomenon. We illustrate an attempt to capture complex model characteristics by analysing (multiple order) correlations in a high dimensional space of parameters of the (biological) system being studied. Both temporal information, among others local scaling information, and external descriptors/parameters, possibly influencing the system's state, are used to span the search space investigated for the presence of a (sub-)optimal model. As an example, we use fetal heartbeat monitored during labour.

Systems Approaches to Cancer Biology.

PubMed

Archer, Tenley C; Fertig, Elana J; Gosline, Sara J C; Hafner, Marc; Hughes, Shannon K; Joughin, Brian A; Meyer, Aaron S; Piccolo, Stephen R; Shajahan-Haq, Ayesha N

2016-12-01

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR. ©2016 American Association for Cancer Research.

Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach.

PubMed

van Roekel, Hendrik W H; Rosier, Bas J H M; Meijer, Lenny H H; Hilbers, Peter A J; Markvoort, Albert J; Huck, Wilhelm T S; de Greef, Tom F A

2015-11-07

Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.

Tracking the Resolution of Student Misconceptions about the Central Dogma of Molecular Biology.

PubMed

Briggs, Amy G; Morgan, Stephanie K; Sanderson, Seth K; Schulting, Molly C; Wieseman, Laramie J

2016-12-01

The goal of our study was to track changes in student understanding of the central dogma of molecular biology before and after taking a genetics course. Concept maps require the ability to synthesize new information into existing knowledge frameworks, and so the hypothesis guiding this study was that student performance on concept maps reveals specific central dogma misconceptions gained, lost, and retained by students. Students in a genetics course completed pre- and posttest concept mapping tasks using terms related to the central dogma. Student maps increased in complexity and validity, indicating learning gains in both content and complexity of understanding. Changes in each of the 351 possible connections in the mapping task were tracked for each student. Our students did not retain much about the central dogma from their introductory biology courses, but they did move to more advanced levels of understanding by the end of the genetics course. The information they retained from their introductory courses focused on structural components (e.g., protein is made of amino acids) and not on overall mechanistic components (e.g., DNA comes before RNA, the ribosome makes protein). Students made the greatest gains in connections related to transcription, and they resolved the most prior misconceptions about translation. These concept-mapping tasks revealed that students are able to correct prior misconceptions about the central dogma during an intermediate-level genetics course. From these results, educators can design new classroom interventions to target those aspects of this foundational principle with which students have the most trouble.

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

Cournia, Zoe; Allen, Toby W.; Andricioaei, Ioan

It is fundamental for the flourishing biological cells that membrane proteins mediate the process. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. Here, we present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.

Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale

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

Zabaras, Nicolas J.

2016-11-08

Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.

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.

Major review: Exfoliation syndrome; advances in disease genetics, molecular biology, and epidemiology.

PubMed

Aboobakar, Inas F; Johnson, William M; Stamer, W Daniel; Hauser, Michael A; Allingham, R Rand

2017-01-01

Exfoliation syndrome (XFS) is a common age-related disorder that leads to deposition of extracellular fibrillar material throughout the body. The most recognized disease manifestation is exfoliation glaucoma (XFG), which is a common cause of blindness worldwide. Recent developments in XFS genetics, cell biology and epidemiology have greatly improved our understanding of the etiology of this complex inherited disease. This review summarizes current knowledge of XFS pathogenesis, identifies gaps in knowledge, and discusses areas for future research. Copyright © 2016. Published by Elsevier Ltd.

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.

Building bridges between cellular and molecular structural biology.

PubMed

Patwardhan, Ardan; Brandt, Robert; Butcher, Sarah J; Collinson, Lucy; Gault, David; Grünewald, Kay; Hecksel, Corey; Huiskonen, Juha T; Iudin, Andrii; Jones, Martin L; Korir, Paul K; Koster, Abraham J; Lagerstedt, Ingvar; Lawson, Catherine L; Mastronarde, David; McCormick, Matthew; Parkinson, Helen; Rosenthal, Peter B; Saalfeld, Stephan; Saibil, Helen R; Sarntivijai, Sirarat; Solanes Valero, Irene; Subramaniam, Sriram; Swedlow, Jason R; Tudose, Ilinca; Winn, Martyn; Kleywegt, Gerard J

2017-07-06

The integration of cellular and molecular structural data is key to understanding the function of macromolecular assemblies and complexes in their in vivo context. Here we report on the outcomes of a workshop that discussed how to integrate structural data from a range of public archives. The workshop identified two main priorities: the development of tools and file formats to support segmentation (that is, the decomposition of a three-dimensional volume into regions that can be associated with defined objects), and the development of tools to support the annotation of biological structures.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

TRIENNIAL LACTATION SYMPOSIUM: Nutrigenomics in livestock: Systems biology meets nutrition.

PubMed

Loor, J J; Vailati-Riboni, M; McCann, J C; Zhou, Z; Bionaz, M

2015-12-01

The advent of high-throughput technologies to study an animal's genome, proteome, and metabolome (i.e., "omics" tools) constituted a setback to the use of reductionism in livestock research. More recent development of "next-generation sequencing" tools was instrumental in allowing in-depth studies of the microbiome in the rumen and other sections of the gastrointestinal tract. Omics, along with bioinformatics, constitutes the foundation of modern systems biology, a field of study widely used in model organisms (e.g., rodents, yeast, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of livestock species, including the transition from pregnancy to lactation, in utero development, or postnatal growth. Modern bioinformatic tools capable of discerning functional outcomes and biologically meaningful networks complement the ever-increasing ability to generate large molecular, microbial, and metabolite data sets. Simultaneous visualization of the complex intertissue adaptations to physiological state and nutrition can now be discerned. Studies to understand the linkages between the microbiome and the absorptive epithelium using the integrative approach are emerging. We present examples of new knowledge generated through the application of functional analyses of transcriptomic, proteomic, and metabolomic data sets encompassing nutritional management of dairy cows, pigs, and poultry. Published work to date underscores that the integrative approach across and within tissues may prove useful for fine-tuning nutritional management of livestock. An important goal during this process is to uncover key molecular players involved in the organismal adaptations to nutrition.

In search of the Golden Fleece: Unraveling principles of morphogenesis by studying the integrative biology of skin appendages

PubMed Central

Hughes, Michael W.; Wu, Ping; Jiang, Ting-Xin; Lin, Sung-Jan; Dong, Chen-Yuan; Li, Ang; Hsieh, Fon-Jou; Widelitz, Randall B.; Choung, Cheng Ming

2013-01-01

Summary The mythological story of the Golden Fleece symbolizes the magical regenerative power of skin appendages. Similar to the adventurous pursuit of the Golden Fleece by the multi-talented Argonauts, today we also need an integrated multi-disciplined approach to understand the cellular and molecular processes during development, regeneration and evolution of skin appendages. To this end, we have explored several aspects of skin appendage biology that contribute to the Turing activator / inhibitor model in feather pattern formation, the topo-biological arrangement of stem cells in organ shape determination, the macro-environmental regulation of stem cells in regenerative hair waves, and potential novel molecular pathways in the morphological evolution of feathers. Here we show our current integrative biology efforts to unravel the complex cellular behavior in patterning stem cells and the control of regional specificity in skin appendages. We use feather / scale tissue recombination to demonstrate the timing control of competence and inducibility. Feathers from different body regions are used to study skin regional specificity. Bioinformatic analyses of transcriptome microarrays show the potential involvement of candidate molecular pathways. We further show Hox genes exhibit some region specific expression patterns. To visualize real time events, we applied time-lapse movies, confocal microscopy and multiphoton microscopy to analyze the morphogenesis of cultured embryonic chicken skin explants. These modern imaging technologies reveal unexpectedly complex cellular flow and organization of extracellular matrix molecules in three dimensions. While these approaches are in preliminary stages, this perspective highlights the challenges we face and new integrative tools we will use. Future work will follow these leads to develop a systems biology view and understanding in the morphogenetic principles that govern the development and regeneration of ectodermal organs. PMID:21437328

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

PubMed

Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

Atomic switch networks as complex adaptive systems

NASA Astrophysics Data System (ADS)

Scharnhorst, Kelsey S.; Carbajal, Juan P.; Aguilera, Renato C.; Sandouk, Eric J.; Aono, Masakazu; Stieg, Adam Z.; Gimzewski, James K.

2018-03-01

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

Using the Tools and Resources of the RCSB Protein Data Bank.

PubMed

Costanzo, Luigi Di; Ghosh, Sutapa; Zardecki, Christine; Burley, Stephen K

2016-09-07

The Protein Data Bank (PDB) archive is the worldwide repository of experimentally determined three-dimensional structures of large biological molecules found in all three kingdoms of life. Atomic-level structures of these proteins, nucleic acids, and complex assemblies thereof are central to research and education in molecular, cellular, and organismal biology, biochemistry, biophysics, materials science, bioengineering, ecology, and medicine. Several types of information are associated with each PDB archival entry, including atomic coordinates, primary experimental data, polymer sequence(s), and summary metadata. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves as the U.S. data center for the PDB, distributing archival data and supporting both simple and complex queries that return results. These data can be freely downloaded, analyzed, and visualized using RCSB PDB tools and resources to gain a deeper understanding of fundamental biological processes, molecular evolution, human health and disease, and drug discovery. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Fiber Diffraction of the Prion-Forming Domain HET-s(218-289) Shows Dehydration-Induced Deformation of a Complex Amyloid Structure

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

Wan, William; Stubbs, Gerald

2014-05-01

Amyloids are filamentous protein aggregates that can be formed by many different proteins and are associated with both disease and biological functions. The pathogenicities or biological functions of amyloids are determined by their particular molecular structures, making accurate structural models a requirement for understanding their biological effects. One potential factor that can affect amyloid structures is hydration. Previous studies of simple stacked β-sheet amyloids have suggested that dehydration does not impact structure, but other studies indicated dehydration-related structural changes of a putative water-filled nanotube. Our results show that dehydration significantly affects the molecular structure of the fungal prion-forming domain HET-s(218–289),more » which forms a β-solenoid with no internal solvent-accessible regions. The dehydration-related structural deformation of HET-s(218–289) indicates that water can play a significant role in complex amyloid structures, even when no obvious water-accessible cavities are present.« less

Intrinsic disorder mediates the diverse regulatory functions of the Cdk inhibitor p21

PubMed Central

Wang, Yuefeng; Fisher, John C.; Mathew, Rose; Ou, Li; Otieno, Steve; Sublett, Jack; Xiao, Limin; Chen, Jianhan; Roussel, Martine F.; Kriwacki, Richard W.

2011-01-01

Traditionally, well-defined three-dimensional structure was thought to be essential for protein function. However, myriad biological functions are performed by highly dynamic, intrinsically disordered proteins (IDPs). IDPs often fold upon binding their biological targets and frequently exhibit “binding diversity” by targeting multiple ligands. We sought to understand the physical basis of IDP binding diversity and herein report that the cyclin-dependent kinase (Cdk) inhibitor, p21Cip1, adaptively binds to and inhibits the various Cdk/cyclin complexes that regulate eukaryotic cell division. Based on results from NMR spectroscopy, and biochemical and cellular assays, we show that structural adaptability of a helical sub-domain within p21 termed LH enables two other sub-domains termed D1 and D2 to specifically bind conserved surface features of the cyclin and Cdk subunits, respectively, within otherwise structurally distinct Cdk/cyclin complexes. Adaptive folding upon binding is likely to mediate the diverse biological functions of the thousands of IDPs present in eukaryotes. PMID:21358637

The Mediator Complex and Lipid Metabolism.

PubMed

Zhang, Yi; Xiaoli; Zhao, Xiaoping; Yang, Fajun

2013-03-01

The precise control of gene expression is essential for all biological processes. In addition to DNA-binding transcription factors, numerous transcription cofactors contribute another layer of regulation of gene transcription in eukaryotic cells. One of such transcription cofactors is the highly conserved Mediator complex, which has multiple subunits and is involved in various biological processes through directly interacting with relevant transcription factors. Although the current understanding on the biological functions of Mediator remains incomplete, research in the past decade has revealed an important role of Mediator in regulating lipid metabolism. Such function of Mediator is dependent on specific transcription factors, including peroxisome proliferator-activated receptor-gamma (PPARγ) and sterol regulatory element-binding proteins (SREBPs), which represent the master regulators of lipid metabolism. The medical significance of these findings is apparent, as aberrant lipid metabolism is intimately linked to major human diseases, such as type 2 diabetes and cardiovascular disease. Here, we briefly review the functions and molecular mechanisms of Mediator in regulation of lipid metabolism.

Propolis: A Complex Natural Product with a Plethora of Biological Activities That Can Be Explored for Drug Development

PubMed Central

Silva-Carvalho, Ricardo; Baltazar, Fátima; Almeida-Aguiar, Cristina

2015-01-01

The health industry has always used natural products as a rich, promising, and alternative source of drugs that are used in the health system. Propolis, a natural resinous product known for centuries, is a complex product obtained by honey bees from substances collected from parts of different plants, buds, and exudates in different geographic areas. Propolis has been attracting scientific attention since it has many biological and pharmacological properties, which are related to its chemical composition. Several in vitro and in vivo studies have been performed to characterize and understand the diverse bioactivities of propolis and its isolated compounds, as well as to evaluate and validate its potential. Yet, there is a lack of information concerning clinical effectiveness. The goal of this review is to discuss the potential of propolis for the development of new drugs by presenting published data concerning the chemical composition and the biological properties of this natural compound from different geographic origins. PMID:26106433

Characterizing local biological hotspots in the Gulf of Maine using remote sensing data

NASA Astrophysics Data System (ADS)

Ribera, Marta M.

Researchers increasingly advocate the use of ecosystem-based management (EBM) for managing complex marine ecosystems. This approach requires managers to focus on processes and cross-scale interactions, rather than individual components. However, they often lack appropriate tools and data sources to pursue this change in management approach. One method that has been proposed to understand the ecological complexity inherent in marine ecosystems is the study of biological hotspots. Biological hotspots are locations where organisms from different trophic levels aggregate to feed on abundant supplies, and they are considered a first step toward understanding the processes driving spatial and temporal heterogeneity in marine systems. Biological hotspots are supported by phytoplankton aggregations, which are characterized by high spatial and temporal variability. As a result, methods developed to locate biological hotspots in relatively stable terrestrial systems are not well suited for more dynamic marine ecosystems. The main objective of this thesis is thus to identify and characterize local-scale biological hotspots in the western side of the Gulf of Maine. The first chapter describes a new methodological framework with the steps needed to locate these types of hotspots in marine ecosystems using remote sensing datasets. Then, in the second chapter these hotspots are characterized using a novel metric that uses time series information and spatial statistics to account for both the temporal variability and spatial structure of these marine aggregations. This metric redefines biological hotspots as areas with a high probability of exhibiting positive anomalies of productivity compared to the expected regional seasonal pattern. Finally, the third chapter compares the resulting biological hotspots to fishery-dependent abundance indices of surface and benthic predators to determine the effect of the location and magnitude of phytoplankton aggregations on the rest of the ecosystem. Analyses indicate that the spatial scale and magnitude of biological hotspots in the Gulf of Maine depend on the location and time of the year. Results also show that these hotspots change over time in response to both short-term oceanographic processes and long-term climatic cycles. Finally, the new metric presented here facilitates the spatial comparison between different trophic levels, thus allowing interdisciplinary ecosystem-wide studies.

A Graph Approach to Mining Biological Patterns in the Binding Interfaces.

PubMed

Cheng, Wen; Yan, Changhui

2017-01-01

Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, we present a graph-mining method for discovering biological patterns in the protein-RNA interfaces. We represented known protein-RNA interfaces using graphs and then discovered graph patterns enriched in the interfaces. Comparison of the discovered graph patterns with UniProt annotations showed that the graph patterns had a significant overlap with residue sites that had been proven crucial for the RNA binding by experimental methods. Using 200 patterns as input features, a support vector machine method was able to classify protein surface patches into RNA-binding sites and non-RNA-binding sites with 84.0% accuracy and 88.9% precision. We built a simple scoring function that calculated the total number of the graph patterns that occurred in a protein-RNA interface. That scoring function was able to discriminate near-native protein-RNA complexes from docking decoys with a performance comparable with that of a state-of-the-art complex scoring function. Our work also revealed possible patterns that might be important for binding affinity.

Protonation free energy levels in complex molecular systems.

PubMed

Antosiewicz, Jan M

2008-04-01

All proteins, nucleic acids, and other biomolecules contain residues capable of exchanging protons with their environment. These proton transfer phenomena lead to pH sensitivity of many molecular processes underlying biological phenomena. In the course of biological evolution, Nature has invented some mechanisms to use pH gradients to regulate biomolecular processes inside cells or in interstitial fluids. Therefore, an ability to model protonation equilibria in molecular systems accurately would be of enormous value for our understanding of biological processes and for possible rational influence on them, like in developing pH dependent drugs to treat particular diseases. This work presents a derivation, by thermodynamic and statistical mechanical methods, of an expression for the free energy of a complex molecular system at arbitrary ionization state of its titratable residues. This constitutes one of the elements of modeling protonation equilibria. Starting from a consideration of a simple acid-base equilibrium of a model compound with a single tritratable group, we arrive at an expression which is of general validity for complex systems. The only approximation used in this derivation is the postulating that the interaction energy between any pair of titratable sites does not depend on the protonation states of all the remaining ionizable groups.

Rapid, Optimized Interactomic Screening

PubMed Central

Hakhverdyan, Zhanna; Domanski, Michal; Hough, Loren; Oroskar, Asha A.; Oroskar, Anil R.; Keegan, Sarah; Dilworth, David J.; Molloy, Kelly R.; Sherman, Vadim; Aitchison, John D.; Fenyö, David; Chait, Brian T.; Jensen, Torben Heick; Rout, Michael P.; LaCava, John

2015-01-01

We must reliably map the interactomes of cellular macromolecular complexes in order to fully explore and understand biological systems. However, there are no methods to accurately predict how to capture a given macromolecular complex with its physiological binding partners. Here, we present a screen that comprehensively explores the parameters affecting the stability of interactions in affinity-captured complexes, enabling the discovery of physiological binding partners and the elucidation of their functional interactions in unparalleled detail. We have implemented this screen on several macromolecular complexes from a variety of organisms, revealing novel profiles even for well-studied proteins. Our approach is robust, economical and automatable, providing an inroad to the rigorous, systematic dissection of cellular interactomes. PMID:25938370

The human mouth: oral functions in a social complexity perspective.

PubMed

Eriksen, Harald M; Dimitrov, Vladimir

2003-06-01

In the dental and medical literature, the mouth and oral functions are usually presented in a biomedical context. However, there may be a need for a broader perspective if we are to appreciate the importance of the human mouth as an organ with diverse functions. The paradigm of complexity appears to be of relevance in our understanding the social and psychological characteristics of the human mouth in addition to its biological functions. Examples such as the pleasures of taste, social aspects of eating, the importance of linguistics and communication are illustrations of some of the social and psychological aspects of oral functions. Professional knowledge related to such issues is important in our understanding the patient's priorities and in performing the relevant diagnosis and treatment planning.

Challenges in Developing Models Describing Complex Soil Systems

NASA Astrophysics Data System (ADS)

Simunek, J.; Jacques, D.

2014-12-01

Quantitative mechanistic models that consider basic physical, mechanical, chemical, and biological processes have the potential to be powerful tools to integrate our understanding of complex soil systems, and the soil science community has often called for models that would include a large number of these diverse processes. However, once attempts have been made to develop such models, the response from the community has not always been overwhelming, especially after it discovered that these models are consequently highly complex, requiring not only a large number of parameters, not all of which can be easily (or at all) measured and/or identified, and which are often associated with large uncertainties, but also requiring from their users deep knowledge of all/most of these implemented physical, mechanical, chemical and biological processes. Real, or perceived, complexity of these models then discourages users from using them even for relatively simple applications, for which they would be perfectly adequate. Due to the nonlinear nature and chemical/biological complexity of the soil systems, it is also virtually impossible to verify these types of models analytically, raising doubts about their applicability. Code inter-comparisons, which is then likely the most suitable method to assess code capabilities and model performance, requires existence of multiple models of similar/overlapping capabilities, which may not always exist. It is thus a challenge not only to developed models describing complex soil systems, but also to persuade the soil science community in using them. As a result, complex quantitative mechanistic models are still an underutilized tool in soil science research. We will demonstrate some of the challenges discussed above on our own efforts in developing quantitative mechanistic models (such as HP1/2) for complex soil systems.

Adsorptive removal and separation of chemicals with metal-organic frameworks: Contribution of π-complexation.

PubMed

Khan, Nazmul Abedin; Jhung, Sung Hwa

2017-03-05

Efficient removal and separation of chemicals from the environment has become a vital issue from a biological and environmental point of view. Currently, adsorptive removal/separation is one of the most promising approaches for cleaning purposes. Selective adsorption/removal of various sulfur- and nitrogen-containing compounds, olefins, and π-electron-rich gases via π-complex formation between an adsorbent and adsorbate molecules is very competitive. Porous metal-organic framework (MOF) materials are very promising in the adsorption/separation of various liquids and gases owing to their distinct characteristics. This review summarizes the literature on the adsorptive removal/separation of various π-electron-rich compounds mainly from fuel and gases using MOF materials containing metal ions that are active for π-complexation. Details of the π-complexation, including mechanism, pros/cons, applications, and efficient ways to form the complex, are discussed systematically. For in-depth understanding, molecular orbital calculations regarding charge transfer between the π-complexing species are also explained in a separate section. From this review, readers will gain an understanding of π-complexation for adsorption and separation, especially with MOFs, to develop new insight for future research. Copyright © 2016 Elsevier B.V. All rights reserved.

Increasing algal photosynthetic productivity by integrating ecophysiology with systems biology.

PubMed

Peers, Graham

2014-11-01

Oxygenic photosynthesis is the process by which plants, algae, and cyanobacteria convert sunlight and CO2 into chemical energy and biomass. Previously published estimates suggest that algal photosynthesis is, at best, able to convert approximately 5-7% of incident light energy to biomass and there is opportunity for improvement. Recent analyses of in situ photophysiology in mass cultures of algae and cyanobacteria show that cultivation methods can have detrimental effects on a cell's photophysiology - reinforcing the need to understand the complex responses of cell biology to a highly variable environment. A systems-based approach to understanding the stresses and efficiencies associated with light-energy harvesting, CO2 fixation, and carbon partitioning will be necessary to make major headway toward improving photosynthetic yields. Copyright © 2014 Elsevier Ltd. All rights reserved.

Proteomics for understanding miRNA biology.

PubMed

Huang, Tai-Chung; Pinto, Sneha M; Pandey, Akhilesh

2013-02-01

MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in posttranscriptional regulation of gene expression. Mature miRNAs associate with the RNA interference silencing complex to repress mRNA translation and/or degrade mRNA transcripts. Mass spectrometry-based proteomics has enabled identification of several core components of the canonical miRNA processing pathway and their posttranslational modifications which are pivotal in miRNA regulatory mechanisms. The use of quantitative proteomic strategies has also emerged as a key technique for experimental identification of miRNA targets by allowing direct determination of proteins whose levels are altered because of translational suppression. This review focuses on the role of proteomics and labeling strategies to understand miRNA biology. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Managing Complexity

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

Chassin, David P.; Posse, Christian; Malard, Joel M.

2004-08-01

Physical analogs have shown considerable promise for understanding the behavior of complex adaptive systems, including macroeconomics, biological systems, social networks, and electric power markets. Many of today’s most challenging technical and policy questions can be reduced to a distributed economic control problem. Indeed, economically-based control of large-scale systems is founded on the conjecture that the price-based regulation (e.g., auctions, markets) results in an optimal allocation of resources and emergent optimal system control. This paper explores the state of the art in the use physical analogs for understanding the behavior of some econophysical systems and to deriving stable and robust controlmore » strategies for them. In particular we review and discussion applications of some analytic methods based on the thermodynamic metaphor according to which the interplay between system entropy and conservation laws gives rise to intuitive and governing global properties of complex systems that cannot be otherwise understood.« less

Diapause in ticks of the medically important Ixodes ricinus species complex

PubMed Central

Gray, Jeremy S.; Kahl, Olaf; Lane, Robert S.; Levin, Michael L.; Tsao, Jean I.

2017-01-01

Four members of the Ixodes ricinus species complex, Ixodes pacificus, Ixodes persulcatus, Ixodes ricinus and Ixodes scapularis, have, between them, a worldwide distribution within the northern hemisphere. They are responsible for the transmission of several animal and human pathogens, including the causal agents of Lyme borreliosis, tick-borne encephalitis, human granulocytic anaplasmosis and human babesiosis. Despite the importance of these ticks as vectors, the knowledge and understanding of the role that diapause plays in their complex life cycles are confused and incomplete. In view of the continuing geographic spread of these tick species, as well as the effects of climate change on vector-borne diseases, it is timely encourage research on diapause phenomena to improve understanding of their biology and of pathogen transmission dynamics. In our review we seek to clarify thinking on the topic and to address gaps in our knowledge that require the attention of researchers. PMID:27263092

Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design.

PubMed

Gurung, A B; Bhattacharjee, A; Ajmal Ali, M; Al-Hemaid, F; Lee, Joongku

2017-02-01

Protein-protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein-protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein-protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein-protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein-protein interaction with the objective of normalizing such interactions.

Recent advances in mathematical criminology. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Rodríguez, Nancy

2015-03-01

The use of mathematical tools has long proved to be useful in gaining understanding of complex systems in physics [1]. Recently, many researchers have realized that there is an analogy between emerging phenomena in complex social systems and complex physical or biological systems [4,5,12]. This realization has particularly benefited the modeling and understanding of crime, a ubiquitous phenomena that is far from being understood. In fact, when one is interested in the bulk behavior of patterns that emerge from small and seemingly unrelated interactions as well as decisions that occur at the individual level, the mathematical tools that have been developed in statistical physics, game theory, network theory, dynamical systems, and partial differential equations can be useful in shedding light into the dynamics of these patterns [2-4,6,12].

Contribution to the meaning and understanding of anticipatory systems

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub

2001-06-01

The present article discusses the cybernetic method in the modelling and understanding of complex systems from the epistemological, semantic as well as psychological point of view. Biological and organisational systems are the most important among complex systems. According to Rosen [1] anticipatory systems is another name for complex systems because, in a way, they function to anticipate the future state in order to preserve its structure and functioning. This paper demonstrates a strong analogy between Rosen's modified definition of anticipatory systems [2] and decision-making through simulation in organisational systems. The possible meaning of several models modified in the anticipatory mode will also be discussed as for example: a) The modified Verhaulst Model and its anticipatory modification in the case of the description of human behavior, b) The Prey-Predator Model, and c) The Evans Market Model under different conditions of the demand and supply function.

WE-DE-202-00: Connecting Radiation Physics with Computational Biology

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

NONE

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

PubMed

Benson, Neil

2015-08-01

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

Making the invisible visible.

PubMed

van Steensel, Maurice A M

2016-04-01

In this review, I will discuss how careful scrutiny of genetic skin disorders could help us to understand human biology. Like other organs, the skin and its appendages, such as hairs and teeth, experience fundamental biological processes ranging from lipid metabolism to vesicular transport and cellular migration. However, in contrast to other organ systems, they are accessible and can be studied with relative ease. By visually revealing the functional consequences of single gene defects, genetic skin diseases offer a unique opportunity to study human biology. Here, I will illustrate this concept by discussing how human genetic disorders of skin pigmentation reflect the mechanisms underlying this complex and vital process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Taking the conservation biology perspective to secondary school classrooms.

PubMed

Wyner, Yael; Desalle, Rob

2010-06-01

The influence of conservation biology can be enhanced greatly if it reaches beyond undergraduate biology to students at the middle and high school levels. If a conservation perspective were taught in secondary schools, students who are not interested in biology could be influenced to pursue careers or live lifestyles that would reduce the negative impact of humans on the world. We use what we call the ecology-disrupted approach to transform the topics of conservation biology research into environmental-issue and ecology topics, the major themes of secondary school courses in environmental science. In this model, students learn about the importance and complexity of normal ecological processes by studying what goes wrong when people disrupt them (environmental issues). Many studies published in Conservation Biology are related in some way to the ecological principles being taught in secondary schools. Describing research in conservation biology in the language of ecology curricula in secondary schools can help bring these science stories to the classroom and give them a context in which they can be understood by students. Without this context in the curriculum, a science story can devolve into just another environmental issue that has no immediate effect on the daily lives of students. Nevertheless, if the research is placed in the context of larger ecological processes that are being taught, students can gain a better understanding of ecology and a better understanding of their effect on the world.

Common Origins of Diverse Misconceptions: Cognitive Principles and the Development of Biology Thinking

PubMed Central

Coley, John D.; Tanner, Kimberly D.

2012-01-01

Many ideas in the biological sciences seem especially difficult to understand, learn, and teach successfully. Our goal in this feature is to explore how these difficulties may stem not from the complexity or opacity of the concepts themselves, but from the fact that they may clash with informal, intuitive, and deeply held ways of understanding the world that have been studied for decades by psychologists. We give a brief overview of the field of developmental cognitive psychology. Then, in each of the following sections, we present a number of common challenges faced by students in the biological sciences. These may be in the form of misconceptions, biases, or simply concepts that are difficult to learn and teach, and they occur at all levels of biological analysis (molecular, cellular, organismal, population, and ecosystem). We then introduce the notion of a cognitive construal and discuss specific examples of how these cognitive principles may explain what makes some misconceptions so alluring and some biological concepts so challenging for undergraduates. We will argue that seemingly unrelated misconceptions may have common origins in a single underlying cognitive construal. These ideas emerge from our own ongoing cross-disciplinary conversation, and we think that expanding this conversation to include other biological scientists and educators, as well as other cognitive scientists, could have significant utility in improving biology teaching and learning. PMID:22949417

Common origins of diverse misconceptions: cognitive principles and the development of biology thinking.

PubMed

Coley, John D; Tanner, Kimberly D

2012-01-01

Many ideas in the biological sciences seem especially difficult to understand, learn, and teach successfully. Our goal in this feature is to explore how these difficulties may stem not from the complexity or opacity of the concepts themselves, but from the fact that they may clash with informal, intuitive, and deeply held ways of understanding the world that have been studied for decades by psychologists. We give a brief overview of the field of developmental cognitive psychology. Then, in each of the following sections, we present a number of common challenges faced by students in the biological sciences. These may be in the form of misconceptions, biases, or simply concepts that are difficult to learn and teach, and they occur at all levels of biological analysis (molecular, cellular, organismal, population, and ecosystem). We then introduce the notion of a cognitive construal and discuss specific examples of how these cognitive principles may explain what makes some misconceptions so alluring and some biological concepts so challenging for undergraduates. We will argue that seemingly unrelated misconceptions may have common origins in a single underlying cognitive construal. These ideas emerge from our own ongoing cross-disciplinary conversation, and we think that expanding this conversation to include other biological scientists and educators, as well as other cognitive scientists, could have significant utility in improving biology teaching and learning.

Insects as vectors: systematics and biology.

PubMed

Rodhain, F

2015-04-01

Among the many complex relationships between insects and microorganisms such as viruses, bacteria and parasites, some have resulted in the establishment of biological systems within which the insects act as a biological vector for infectious agents. It is therefore advisable to understand the identity and biology of these vectors in depth, in order to define procedures for epidemiological surveillance and anti-vector control. The following are successively reviewed in this article: Anoplura (lice), Siphonaptera (fleas), Heteroptera (bugs: Cimicidae, Triatoma, Belostomatidae), Psychodidae (sandflies), Simuliidae (black flies), Ceratopogonidae (biting midges), Culicidae (mosquitoes), Tabanidae (horseflies) and Muscidae (tsetse flies, stable flies and pupipara). The authors provide a rapid overview of the morphology, systematics, development cycle and bio-ecology of each of these groups of vectors. Finally, their medical and veterinary importance is briefly reviewed.

E-Index for Differentiating Complex Dynamic Traits

PubMed Central

Qi, Jiandong; Sun, Jianfeng; Wang, Jianxin

2016-01-01

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

Experimental and Theoretical Approaches for the Surface Interaction between Copper and Activated Sludge Microorganisms at Molecular Scale

NASA Astrophysics Data System (ADS)

Luo, Hong-Wei; Chen, Jie-Jie; Sheng, Guo-Ping; Su, Ji-Hu; Wei, Shi-Qiang; Yu, Han-Qing

2014-11-01

Interactions between metals and activated sludge microorganisms substantially affect the speciation, immobilization, transport, and bioavailability of trace heavy metals in biological wastewater treatment plants. In this study, the interaction of Cu(II), a typical heavy metal, onto activated sludge microorganisms was studied in-depth using a multi-technique approach. The complexing structure of Cu(II) on microbial surface was revealed by X-ray absorption fine structure (XAFS) and electron paramagnetic resonance (EPR) analysis. EPR spectra indicated that Cu(II) was held in inner-sphere surface complexes of octahedral coordination with tetragonal distortion of axial elongation. XAFS analysis further suggested that the surface complexation between Cu(II) and microbial cells was the distorted inner-sphere coordinated octahedra containing four short equatorial bonds and two elongated axial bonds. To further validate the results obtained from the XAFS and EPR analysis, density functional theory calculations were carried out to explore the structural geometry of the Cu complexes. These results are useful to better understand the speciation, immobilization, transport, and bioavailability of metals in biological wastewater treatment plants.

Active control of complex, multicomponent self-assembly processes

NASA Astrophysics Data System (ADS)

Schulman, Rebecca

The kinetics of many complex biological self-assembly processes such as cytoskeletal assembly are precisely controlled by cells. Spatiotemporal control over rates of filament nucleation, growth and disassembly determine how self-assembly occurs and how the assembled form changes over time. These reaction rates can be manipulated by changing the concentrations of the components needed for assembly by activating or deactivating them. I will describe how we can use these principles to design driven self-assembly processes in which we assemble and disassemble multiple types of components to create micron-scale networks of semiflexible filaments assembled from DNA. The same set of primitive components can be assembled into many different, structures depending on the concentrations of different components and how designed, DNA-based chemical reaction networks manipulate these concentrations over time. These chemical reaction networks can in turn interpret environmental stimuli to direct complex, multistage response. Such a system is a laboratory for understanding complex active material behaviors, such as metamorphosis, self-healing or adaptation to the environment that are ubiquitous in biological systems but difficult to quantitatively characterize or engineer.

Proteomics and Systems Biology: Current and Future Applications in the Nutritional Sciences1

PubMed Central

Moore, J. Bernadette; Weeks, Mark E.

2011-01-01

In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences. PMID:22332076

Neuroscience, moral reasoning, and the law.

PubMed

Knabb, Joshua J; Welsh, Robert K; Ziebell, Joseph G; Reimer, Kevin S

2009-01-01

Modern advancements in functional magnetic resonance imaging (fMRI) technology have given neuroscientists the opportunity to more fully appreciate the brain's contribution to human behavior and decision making. Morality and moral reasoning are relative newcomers to the growing literature on decision neuroscience. With recent attention given to the salience of moral factors (e.g. moral emotions, moral reasoning) in the process of decision making, neuroscientists have begun to offer helpful frameworks for understanding the interplay between the brain, morality, and human decision making. These frameworks are relatively unfamiliar to the community of forensic psychologists, despite the fact that they offer an improved understanding of judicial decision making from a biological perspective. This article presents a framework reviewing how event-feature-emotion complexes (EFEC) are relevant to jurors and understanding complex criminal behavior. Future directions regarding converging fields of neuroscience and legal decision making are considered. Copyright 2009 John Wiley & Sons, Ltd.

Why (and how) should we study the interplay between emotional arousal, Theory of Mind, and inhibitory control to understand moral cognition?

PubMed

Buon, Marine; Seara-Cardoso, Ana; Viding, Essi

2016-12-01

Findings in the field of experimental psychology and cognitive neuroscience have shed new light on our understanding of the psychological and biological bases of morality. Although a lot of attention has been devoted to understanding the processes that underlie complex moral dilemmas, attempts to represent the way in which individuals generate moral judgments when processing basic harmful actions are rare. Here, we will outline a model of morality which proposes that the evaluation of basic harmful actions relies on complex interactions between emotional arousal, Theory of Mind (ToM) capacities, and inhibitory control resources. This model makes clear predictions regarding the cognitive processes underlying the development of and ability to generate moral judgments. We draw on data from developmental and cognitive psychology, cognitive neuroscience, and psychopathology research to evaluate the model and propose several conceptual and methodological improvements that are needed to further advance our understanding of moral cognition and its development.

Fundamental Challenges in Mechanistic Enzymology: Progress toward Understanding the Rate Enhancements of Enzymes

PubMed Central

Herschlag, Daniel; Natarajan, Aditya

2013-01-01

Enzymes are remarkable catalysts that lie at the heart of biology, accelerating chemical reactions to an astounding extent with extraordinary specificity. Enormous progress in understanding the chemical basis of enzymatic transformations and the basic mechanisms underlying rate enhancements over the past decades is apparent. Nevertheless, it has been difficult to achieve a quantitative understanding of how the underlying mechanisms account for the energetics of catalysis, because of the complexity of enzyme systems and the absence of underlying energetic additivity. We review case studies from our own work that illustrate the power of precisely defined and clearly articulated questions when dealing with such complex and multi-faceted systems, and we also use this approach to evaluate our current ability to design enzymes. We close by highlighting a series of questions that help frame some of what remains to be understood, and we encourage the reader to define additional questions and directions that will deepen and broaden our understanding of enzymes and their catalysis. PMID:23488725

Fundamental challenges in mechanistic enzymology: progress toward understanding the rate enhancements of enzymes.

PubMed

Herschlag, Daniel; Natarajan, Aditya

2013-03-26

Enzymes are remarkable catalysts that lie at the heart of biology, accelerating chemical reactions to an astounding extent with extraordinary specificity. Enormous progress in understanding the chemical basis of enzymatic transformations and the basic mechanisms underlying rate enhancements over the past decades is apparent. Nevertheless, it has been difficult to achieve a quantitative understanding of how the underlying mechanisms account for the energetics of catalysis, because of the complexity of enzyme systems and the absence of underlying energetic additivity. We review case studies from our own work that illustrate the power of precisely defined and clearly articulated questions when dealing with such complex and multifaceted systems, and we also use this approach to evaluate our current ability to design enzymes. We close by highlighting a series of questions that help frame some of what remains to be understood, and we encourage the reader to define additional questions and directions that will deepen and broaden our understanding of enzymes and their catalysis.

Systems medicine: a new approach to clinical practice.

PubMed

Cardinal-Fernández, Pablo; Nin, Nicolás; Ruíz-Cabello, Jesús; Lorente, José A

2014-10-01

Most respiratory diseases are considered complex diseases as their susceptibility and outcomes are determined by the interaction between host-dependent factors (genetic factors, comorbidities, etc.) and environmental factors (exposure to microorganisms or allergens, treatments received, etc.) The reductionist approach in the study of diseases has been of fundamental importance for the understanding of the different components of a system. Systems biology or systems medicine is a complementary approach aimed at analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels. Systems medicine is currently used for the interpretation and understanding of the pathogenesis and pathophysiology of different diseases, biomarker discovery, design of innovative therapeutic targets, and the drawing up of computational models for different biological processes. In this review we discuss the most relevant concepts of the theory underlying systems medicine, as well as its applications in the various biological processes in humans. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

The impact of structural biology in medicine illustrated with four case studies.

PubMed

Hu, Tiancen; Sprague, Elizabeth R; Fodor, Michelle; Stams, Travis; Clark, Kirk L; Cowan-Jacob, Sandra W

2018-01-01

The contributions of structural biology to drug discovery have expanded over the last 20 years from structure-based ligand optimization to a broad range of clinically relevant topics including the understanding of disease, target discovery, screening for new types of ligands, discovery of new modes of action, addressing clinical challenges such as side effects or resistance, and providing data to support drug registration. This expansion of scope is due to breakthroughs in the technology, which allow structural information to be obtained rapidly and for more complex molecular systems, but also due to the combination of different technologies such as X-ray, NMR, and other biophysical methods, which allows one to get a more complete molecular understanding of disease and ways to treat it. In this review, we provide examples of the types of impact molecular structure information can have in the clinic for both low molecular weight and biologic drug discovery and describe several case studies from our own work to illustrate some of these contributions.

The cell biology of Tobacco mosaic virus replication and movement

PubMed Central

Liu, Chengke; Nelson, Richard S.

2013-01-01

Successful systemic infection of a plant by Tobacco mosaic virus (TMV) requires three processes that repeat over time: initial establishment and accumulation in invaded cells, intercellular movement, and systemic transport. Accumulation and intercellular movement of TMV necessarily involves intracellular transport by complexes containing virus and host proteins and virus RNA during a dynamic process that can be visualized. Multiple membranes appear to assist TMV accumulation, while membranes, microfilaments and microtubules appear to assist TMV movement. Here we review cell biological studies that describe TMV-membrane, -cytoskeleton, and -other host protein interactions which influence virus accumulation and movement in leaves and callus tissue. The importance of understanding the developmental phase of the infection in relationship to the observed virus-membrane or -host protein interaction is emphasized. Utilizing the latest observations of TMV-membrane and -host protein interactions within our evolving understanding of the infection ontogeny, a model for TMV accumulation and intracellular spread in a cell biological context is provided. PMID:23403525

Pydna: a simulation and documentation tool for DNA assembly strategies using python.

PubMed

Pereira, Filipa; Azevedo, Flávio; Carvalho, Ângela; Ribeiro, Gabriela F; Budde, Mark W; Johansson, Björn

2015-05-02

Recent advances in synthetic biology have provided tools to efficiently construct complex DNA molecules which are an important part of many molecular biology and biotechnology projects. The planning of such constructs has traditionally been done manually using a DNA sequence editor which becomes error-prone as scale and complexity of the construction increase. A human-readable formal description of cloning and assembly strategies, which also allows for automatic computer simulation and verification, would therefore be a valuable tool. We have developed pydna, an extensible, free and open source Python library for simulating basic molecular biology DNA unit operations such as restriction digestion, ligation, PCR, primer design, Gibson assembly and homologous recombination. A cloning strategy expressed as a pydna script provides a description that is complete, unambiguous and stable. Execution of the script automatically yields the sequence of the final molecule(s) and that of any intermediate constructs. Pydna has been designed to be understandable for biologists with limited programming skills by providing interfaces that are semantically similar to the description of molecular biology unit operations found in literature. Pydna simplifies both the planning and sharing of cloning strategies and is especially useful for complex or combinatorial DNA molecule construction. An important difference compared to existing tools with similar goals is the use of Python instead of a specifically constructed language, providing a simulation environment that is more flexible and extensible by the user.

X-ray absorption spectroscopy to watch catalysis by metalloenzymes: status and perspectives discussed for the water-splitting manganese complex of photosynthesis.

PubMed

Dau, Holger; Haumann, Michael

2003-01-01

Understanding structure-function relations is one of the main interests in the molecular biosciences. X-ray absorption spectroscopy of biological samples (BioXAS) has gained the status of a useful tool for characterization of the structure of protein-bound metal centers with respect to the electronic structure (oxidation states, orbital occupancies) and atomic structure (arrangement of ligand atoms). Owing to progress in the performance characteristics of synchrotron radiation sources and of experimental stations dedicated to the study of (ultra-dilute) biological samples, it is now possible to carry out new types of BioXAS experiments, which have been impracticable in the past. Of particular interest are approaches to follow biological catalysis at metal sites by characterization of functionally relevant structural changes. In this Article, the first steps towards the use of BioXAS to 'watch' biological catalysis are reviewed for the water-splitting reactions occurring at the manganese complex of photosynthesis. The following aspects are considered: the role of BioXAS in life sciences; methodological aspects of BioXAS; catalysis at the Mn complex of photosynthesis; combination of EXAFS and crystallographic information; the freeze-quench technique to capture semi-stable states; time-resolved BioXAS using a freeze-quench approach; room-temperature experiments and 'real-time' BioXAS; tasks and perspectives.

Cytomics - importance of multimodal analysis of cell function and proliferation in oncology.

PubMed

Tárnok, A; Bocsi, J; Brockhoff, G

2006-12-01

Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

Palatal Seam Disintegration: To Die or Not to Die? That Is No Longer the Question

PubMed Central

Nawshad, Ali

2008-01-01

Formation of the medial epithelial seam (MES) by palatal shelf fusion is a crucial step of palate development. Complete disintegration of the MES is the final essential phase of palatal confluency with surrounding mesenchymal cells. In general, the mechanisms of palatal seam disintegration are not overwhelmingly complex, but given the large number of interacting constituents; their complicated circuitry involving feedforward, feedback, and crosstalk; and the fact that the kinetics of interaction matter, this otherwise simple mechanism can be quite difficult to interpret. As a result of this complexity, apparently simple but highly important questions remain unanswered. One such question pertains to the fate of the palatal seam. Such questions may be answered by detailed and extensive quantitative experimentation of basic biological studies (cellular, structural) and the newest molecular biological determinants (genetic/dye cell lineage, gene activity, kinase/enzyme activity), as well as animal model (knockouts, transgenic) approaches. System biology and cellular kinetics play a crucial role in cellular MES function; omissions of such critical contributors may lead to inaccurate understanding of the fate of MES. Excellent progress has been made relevant to elucidation of the mechanism(s) of palatal seam disintegration. Current understanding of palatal seam disintegration suggests epithelial–mesenchymal transition and/or programmed cell death as two most common mechanisms of MES disintegration. In this review, I discuss those two mechanisms and the differences between them. PMID:18629865

On the spatial heterogeneity of net ecosystem productivity in complex landscapes

Treesearch

Ryan E. Emanuel; Diego A. Riveros-Iregui; Brian L. McGlynn; Howard E. Epstein

2011-01-01

Micrometeorological flux towers provide spatially integrated estimates of net ecosystem production (NEP) of carbon over areas ranging from several hectares to several square kilometers, but they do so at the expense of spatially explicit information within the footprint of the tower. This finer-scale information is crucial for understanding how physical and biological...

The Effects of an Interdisciplinary Undergraduate Human Biology Program on Socioscientific Reasoning, Content Learning, and Understanding of Inquiry

ERIC Educational Resources Information Center

Eastwood, Jennifer L.

2010-01-01

Preparing students to take informed positions on complex problems through critical evaluation is a primary goal of university education. Socioscientific issues (SSI) have been established as effective contexts for students to develop this competency, as well as reasoning skills and content knowledge. This mixed-methods study investigates the…

Fundamental Physics Program and the NASA Mission

NASA Technical Reports Server (NTRS)

Trinh, Eugene

2003-01-01

The accomplishments of Physics, the increasing power of its instruments, and its expanding reach into other sciences have generated an unprecedented set of scientific opportunities. The committee has identified six such Grand Challenges listed below in no particular order: Developing quantum technologies. Creating new materials. Understanding complex systems. Unifying the forces of Nature. Exploring the universe Applying Physics to Biology.

A Working Model of Natural Selection Illustrated by Table Tennis

ERIC Educational Resources Information Center

Dinc, Muhittin; Kilic, Selda; Aladag, Caner

2013-01-01

Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…

Cognitive Flexibility and Undergraduate Physiology Students: Increasing Advanced Knowledge Acquisition within an Ill-Structured Domain

ERIC Educational Resources Information Center

Rhodes, Ashley E.; Rozell, Timothy G.

2017-01-01

Cognitive flexibility is defined as the ability to assimilate previously learned information and concepts to generate novel solutions to new problems. This skill is crucial for success within ill-structured domains such as biology, physiology, and medicine, where many concepts are simultaneously required for understanding a complex problem, yet…

A Tale of Two Fishes or a Quick Fix for Fick's Law

ERIC Educational Resources Information Center

Robischon, Marcel A.

2014-01-01

Gas diffusion, as a basis for complex biological processes such as respiration, is a core principle for understanding fundamental physiology. Students, however, often find these concepts challenging, in particular when expressed formally as in Fick's law of gas diffusion. This introduction to Fick's law uses the representations of…

Teaching Energy Concepts by Working on Themes of Cultural and Environmental Value

ERIC Educational Resources Information Center

Besson, Ugo; De Ambrosis, Anna

2014-01-01

Energy is a central topic in physics and a key concept for understanding the physical, biological and technological worlds. It is a complex topic with multiple connections with different areas of science and with social, environmental and philosophical issues. In this paper we discuss some aspects of the teaching and learning of the energy…

Malthus under a Microscope: Using the Soil Nematode "Coenorhabditis elegans" to Test Darwin's Premises about Populations

ERIC Educational Resources Information Center

Mueller, Melinda M.

2007-01-01

Biological evolution is one of the over-arching concepts recommended for student learning by the "National Science Education Standards." As with all such complex concepts, student understanding of evolution is improved when instruction includes hands-on, inquiry-based activities. However, even authors writing in strong support of teaching…

From Purines to Basic Biochemical Concepts: Experiments for High School Students

ERIC Educational Resources Information Center

Marini, Isabella; Ipata, Piero Luigi

2007-01-01

Many high school biology courses address mainly the molecular and cellular basis of life. The complexity that underlies the most essential processes is often difficult for the students to understand; possibly, in part, because of the inability to see and explore them. Six simple practical experiments on purine catabolism as a part of a…

Structure and Function of Wood

Treesearch

Alex C. Wiedenhoeft

2012-01-01

Wood is a complex biological structure, a composite of many cell types and chemistries acting together to serve the needs of living plant. Attempting to understand wood inthe context of wood technology, we have often overlooked the basic fact that wood evolved over the course of millions of years to serve three main functions in plants-conduction of water from the...

Student Teachers' Pedagogical Content Knowledge for Teaching Systems Thinking: Effects of Different Interventions

ERIC Educational Resources Information Center

Rosenkränzer, Frank; Hörsch, Christian; Schuler, Stephan; Riess, Werner

2017-01-01

Systems' thinking has become increasingly relevant not only in education for sustainable development but also in everyday life. Even if teachers know the dynamics and complexity of living systems in biology and geography, they might not be able to effectively explain it to students. Teachers need an understanding of systems and their behaviour…

Strand IV Environmental and Community Health, Ecology and Epidemiology of Health, Grades 10, 11, and 12.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Bureau of Secondary Curriculum Development.

A frame of reference concerning health implications, based on the interaction of numerous factors in the physical, social, and biological environments, is provided in this prototype curriculum for grades 10-12. Development of sound techniques in problem solving is encouraged, resulting from the need to understand the nature and complexities of…

Protein-protein interaction networks (PPI) and complex diseases

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

2014-01-01

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

Visualizing chaperone-assisted protein folding

DOE PAGES

Horowitz, Scott; Salmon, Loïc; Koldewey, Philipp; ...

2016-05-30

We present that challenges in determining the structures of heterogeneous and dynamic protein complexes have greatly hampered past efforts to obtain a mechanistic understanding of many important biological processes. One such process is chaperone-assisted protein folding. Obtaining structural ensembles of chaperone–substrate complexes would ultimately reveal how chaperones help proteins fold into their native state. To address this problem, we devised a new structural biology approach based on X-ray crystallography, termed residual electron and anomalous density (READ). READ enabled us to visualize even sparsely populated conformations of the substrate protein immunity protein 7 (Im7) in complex with the Escherichia coli chaperonemore » Spy, and to capture a series of snapshots depicting the various folding states of Im7 bound to Spy. The ensemble shows that Spy-associated Im7 samples conformations ranging from unfolded to partially folded to native-like states and reveals how a substrate can explore its folding landscape while being bound to a chaperone.« less

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

Computational Study of the Genomic and Epigenomic Phenomena

NASA Astrophysics Data System (ADS)

Yang, Wenjing

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

Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

PubMed Central

2013-01-01

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.

PubMed

Vella, Danila; Zoppis, Italo; Mauri, Giancarlo; Mauri, Pierluigi; Di Silvestre, Dario

2017-12-01

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Development and Effectiveness of an Educational Card Game as Supplementary Material in Understanding Selected Topics in Biology

PubMed Central

Gutierrez, Arnel F.

2014-01-01

The complex concepts and vocabulary of biology classes discourage many students. In this study, a pretest–posttest model was used to test the effectiveness of an educational card game in reinforcing biological concepts in comparison with traditional teaching methods. The subjects of this study were two biology classes at Bulacan State University–Sarmiento Campus. Both classes received conventional instruction; however, the experimental group's instruction was supplemented with the card game, while the control group's instruction was reinforced with traditional exercises and assignments. The score increases from pretest to posttest showed that both methods effectively reinforced biological concepts, but a t test showed that the card game is more effective than traditional teaching methods. Additionally, students from the experimental group evaluated the card game using five criteria: goals, design, organization, playability, and usefulness. The students rated the material very satisfactory. PMID:24591506

Mechanisms of bacterial morphogenesis: evolutionary cell biology approaches provide new insights.

PubMed

Jiang, Chao; Caccamo, Paul D; Brun, Yves V

2015-04-01

How Darwin's "endless forms most beautiful" have evolved remains one of the most exciting questions in biology. The significant variety of bacterial shapes is most likely due to the specific advantages they confer with respect to the diverse environments they occupy. While our understanding of the mechanisms generating relatively simple shapes has improved tremendously in the last few years, the molecular mechanisms underlying the generation of complex shapes and the evolution of shape diversity are largely unknown. The emerging field of bacterial evolutionary cell biology provides a novel strategy to answer this question in a comparative phylogenetic framework. This relatively novel approach provides hypotheses and insights into cell biological mechanisms, such as morphogenesis, and their evolution that would have been difficult to obtain by studying only model organisms. We discuss the necessary steps, challenges, and impact of integrating "evolutionary thinking" into bacterial cell biology in the genomic era. © 2015 WILEY Periodicals, Inc.

Development and effectiveness of an educational card game as supplementary material in understanding selected topics in biology.

PubMed

Gutierrez, Arnel F

2014-01-01

The complex concepts and vocabulary of biology classes discourage many students. In this study, a pretest-posttest model was used to test the effectiveness of an educational card game in reinforcing biological concepts in comparison with traditional teaching methods. The subjects of this study were two biology classes at Bulacan State University-Sarmiento Campus. Both classes received conventional instruction; however, the experimental group's instruction was supplemented with the card game, while the control group's instruction was reinforced with traditional exercises and assignments. The score increases from pretest to posttest showed that both methods effectively reinforced biological concepts, but a t test showed that the card game is more effective than traditional teaching methods. Additionally, students from the experimental group evaluated the card game using five criteria: goals, design, organization, playability, and usefulness. The students rated the material very satisfactory.

Synergizing Engineering and Biology to Treat and Model Skeletal Muscle Injury and Disease

PubMed Central

Bursac, Nenad; Juhas, Mark; Rando, Thomas A.

2016-01-01

Although skeletal muscle is one of the most regenerative organs in our body, various genetic defects, alterations in extrinsic signaling, or substantial tissue damage can impair muscle function and the capacity for self-repair. The diversity and complexity of muscle disorders have attracted much interest from both cell biologists and, more recently, bioengineers, leading to concentrated efforts to better understand muscle pathology and develop more efficient therapies. This review describes the biological underpinnings of muscle development, repair, and disease, and discusses recent bioengineering efforts to design and control myomimetic environments, both to study muscle biology and function and to aid in the development of new drug, cell, and gene therapies for muscle disorders. The synergy between engineering-aided biological discovery and biology-inspired engineering solutions will be the path forward for translating laboratory results into clinical practice. PMID:26643021

Learning cell biology as a team: a project-based approach to upper-division cell biology.

PubMed

Wright, Robin; Boggs, James

2002-01-01

To help students develop successful strategies for learning how to learn and communicate complex information in cell biology, we developed a quarter-long cell biology class based on team projects. Each team researches a particular human disease and presents information about the cellular structure or process affected by the disease, the cellular and molecular biology of the disease, and recent research focused on understanding the cellular mechanisms of the disease process. To support effective teamwork and to help students develop collaboration skills useful for their future careers, we provide training in working in small groups. A final poster presentation, held in a public forum, summarizes what students have learned throughout the quarter. Although student satisfaction with the course is similar to that of standard lecture-based classes, a project-based class offers unique benefits to both the student and the instructor.

The Effectiveness of an Online Curriculum on High School Students' Understanding of Biological Evolution

NASA Astrophysics Data System (ADS)

Marsteller, Robert B.; Bodzin, Alec M.

2015-12-01

An online curriculum about biological evolution was designed to promote increased student content knowledge and evidentiary reasoning. A feasibility study was conducted with 77 rural high school biology students who learned with the online biological evolution unit. Data sources included the Biological Evolution Assessment Measure (BEAM), an analysis of discussion forum posts, and a post-implementation perceptions and attitudes questionnaire. BEAM posttest scores were significantly higher than the pretest scores. However, the findings revealed that the students required additional support to develop evidentiary reasoning. Many students perceived that the Web-based curriculum would have been enhanced by increased immediate interaction and feedback. Students required greater scaffolding to support complex, process-oriented tasks. Implications for designing Web-based science instruction with curriculum materials to support students' acquisition of content knowledge and science process skills in a Web-based setting are discussed.

WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

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

Stewart, R.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

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

McMahon, S.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

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

Schuemann, J.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

Metastable coexistence of multiple genotypes in a constant environment with a single resource through fixed settings of a multiplication-survival trade-off.

PubMed

Maharjan, Ram; Ferenci, Thomas

2016-04-01

The biological complexity of trade-offs has been a major obstacle in understanding bacterial diversity and coexistence. Here we reduce the biological complexity by using isogenic Escherichia coli strains differing only in a multiplication-survival trade-off regulated by RpoS. The contribution of trade-off characteristics to fitness in different environments was determined. We then designed an environment with intermediate-stress levels that elicits an equivalent fitness. We found metastable coexistence of three strains in steady-state chemostats until mutations changed the relative fitness of competing strains. Our results help explain the rich intra- and inter-species diversity of bacteria through alternative settings of relatively few trade-offs. Copyright © 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Objects and processes: Two notions for understanding biological information.

PubMed

Mercado-Reyes, Agustín; Padilla-Longoria, Pablo; Arroyo-Santos, Alfonso

2015-09-07

In spite of being ubiquitous in life sciences, the concept of information is harshly criticized. Uses of the concept other than those derived from Shannon׳s theory are denounced as metaphoric. We perform a computational experiment to explore whether Shannon׳s information is adequate to describe the uses of said concept in commonplace scientific practice. Our results show that semantic sequences do not have unique complexity values different from the value of meaningless sequences. This result suggests that quantitative theoretical frameworks do not account fully for the complex phenomenon that the term "information" refers to. We propose a restructuring of the concept into two related, but independent notions, and conclude that a complete theory of biological information must account completely not only for both notions, but also for the relationship between them. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry.

PubMed

Caron, Etienne; Kowalewski, Daniel J; Chiek Koh, Ching; Sturm, Theo; Schuster, Heiko; Aebersold, Ruedi

2015-12-01

The myriad of peptides presented at the cell surface by class I and class II major histocompatibility complex (MHC) molecules are referred to as the immunopeptidome and are of great importance for basic and translational science. For basic science, the immunopeptidome is a critical component for understanding the immune system; for translational science, exact knowledge of the immunopeptidome can directly fuel and guide the development of next-generation vaccines and immunotherapies against autoimmunity, infectious diseases, and cancers. In this mini-review, we summarize established isolation techniques as well as emerging mass spectrometry-based platforms (i.e. SWATH-MS) to identify and quantify MHC-associated peptides. We also highlight selected biological applications and discuss important current technical limitations that need to be solved to accelerate the development of this field. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Epigenomics and the concept of degeneracy in biological systems

PubMed Central

Mason, Paul H.; Barron, Andrew B.

2014-01-01

Researchers in the field of epigenomics are developing more nuanced understandings of biological complexity, and exploring the multiple pathways that lead to phenotypic expression. The concept of degeneracy—referring to the multiple pathways that a system recruits to achieve functional plasticity—is an important conceptual accompaniment to the growing body of knowledge in epigenomics. Distinct from degradation, redundancy and dilapidation; degeneracy refers to the plasticity of traits whose function overlaps in some environments, but diverges in others. While a redundant system is composed of repeated identical elements performing the same function, a degenerate system is composed of different elements performing similar or overlapping functions. Here, we describe the degenerate structure of gene regulatory systems from the basic genetic code to flexible epigenomic modifications, and discuss how these structural features have contributed to organism complexity, robustness, plasticity and evolvability. PMID:24335757

Biocellion: accelerating computer simulation of multicellular biological system models

PubMed Central

Kang, Seunghwa; Kahan, Simon; McDermott, Jason; Flann, Nicholas; Shmulevich, Ilya

2014-01-01

Motivation: Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. Results: We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Availability and implementation: Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information. Contact: seunghwa.kang@pnnl.gov PMID:25064572

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

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

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

DNAproDB: an interactive tool for structural analysis of DNA–protein complexes

PubMed Central

Sagendorf, Jared M.

2017-01-01

Abstract Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu. PMID:28431131

Pathogenesis of Crohn's disease

PubMed Central

Boyapati, Ray; Satsangi, Jack

2015-01-01

Significant progress in our understanding of Crohn's disease (CD), an archetypal common, complex disease, has now been achieved. Our ability to interrogate the deep complexities of the biological processes involved in maintaining gut mucosal homeostasis is a major over-riding factor underpinning this rapid progress. Key studies now offer many novel and expansive insights into the interacting roles of genetic susceptibility, immune function, and the gut microbiota in CD. Here, we provide overviews of these recent advances and new mechanistic themes, and address the challenges and prospects for translation from concept to clinic. PMID:26097717

The Hidden Complexity of Biological "Dirty Bombs": Implications for Special Operations Medical Personnel.

PubMed

Washington, Michael A; Blythe, Jauchia

The recent capture of a terrorist in Belgium carrying explosives, fecal matter, and animal tissue may indicate a shift from conventional weapons to crude bacteriological preparations as instruments of terror. It is important to note that although such weapons lack technological sophistication, bacteria are inherently complex, unpredictable, and undetectable in the field. Therefore, it is important that Special Operations medical personnel understand the complications that such seemingly simple devices can add to the treatment of casualties in the field and subsequent evaluation in the clinic. 2016.

RNA and RNP as Building Blocks for Nanotechnology and Synthetic Biology.

PubMed

Ohno, Hirohisa; Saito, Hirohide

2016-01-01

Recent technologies that aimed to elucidate cellular function have revealed essential roles for RNA molecules in living systems. Our knowledge concerning functional and structural information of naturally occurring RNA and RNA-protein (RNP) complexes is increasing rapidly. RNA and RNP interaction motifs are structural units that function as building blocks to constitute variety of complex structures. RNA-central synthetic biology and nanotechnology are constructive approaches that employ the accumulated information and build synthetic RNA (RNP)-based circuits and nanostructures. Here, we describe how to design and construct synthetic RNA (RNP)-based devices and structures at the nanometer-scale for biological and future therapeutic applications. RNA/RNP nanostructures can also be utilized as the molecular scaffold to control the localization or interactions of target molecule(s). Moreover, RNA motifs recognized by RNA-binding proteins can be applied to make protein-responsive translational "switches" that can turn gene expression "on" or "off" depending on the intracellular environment. This "synthetic RNA and RNP world" will expand tools for nanotechnology and synthetic biology. In addition, these reconstructive approaches would lead to a greater understanding of building principle in naturally occurring RNA/RNP molecules and systems. Copyright © 2016 Elsevier Inc. All rights reserved.

Nitric oxide and nitrous oxide turnover in natural and engineered microbial communities: biological pathways, chemical reactions, and novel technologies

PubMed Central

Schreiber, Frank; Wunderlin, Pascal; Udert, Kai M.; Wells, George F.

2012-01-01

Nitrous oxide (N2O) is an environmentally important atmospheric trace gas because it is an effective greenhouse gas and it leads to ozone depletion through photo-chemical nitric oxide (NO) production in the stratosphere. Mitigating its steady increase in atmospheric concentration requires an understanding of the mechanisms that lead to its formation in natural and engineered microbial communities. N2O is formed biologically from the oxidation of hydroxylamine (NH2OH) or the reduction of nitrite (NO−2) to NO and further to N2O. Our review of the biological pathways for N2O production shows that apparently all organisms and pathways known to be involved in the catabolic branch of microbial N-cycle have the potential to catalyze the reduction of NO−2 to NO and the further reduction of NO to N2O, while N2O formation from NH2OH is only performed by ammonia oxidizing bacteria (AOB). In addition to biological pathways, we review important chemical reactions that can lead to NO and N2O formation due to the reactivity of NO−2, NH2OH, and nitroxyl (HNO). Moreover, biological N2O formation is highly dynamic in response to N-imbalance imposed on a system. Thus, understanding NO formation and capturing the dynamics of NO and N2O build-up are key to understand mechanisms of N2O release. Here, we discuss novel technologies that allow experiments on NO and N2O formation at high temporal resolution, namely NO and N2O microelectrodes and the dynamic analysis of the isotopic signature of N2O with quantum cascade laser absorption spectroscopy (QCLAS). In addition, we introduce other techniques that use the isotopic composition of N2O to distinguish production pathways and findings that were made with emerging molecular techniques in complex environments. Finally, we discuss how a combination of the presented tools might help to address important open questions on pathways and controls of nitrogen flow through complex microbial communities that eventually lead to N2O build-up. PMID:23109930

How Does Student Understanding of a Concept Change Throughout a Unit of Instruction? Support Toward the Theory of Learning Progressions

NASA Astrophysics Data System (ADS)

Dyer, Brian Jay

This study documented the changes in understanding a class of eighth grade high school-level biology students experienced through a biology unit introducing genetics. Learning profiles for 55 students were created using concept maps and interviews as qualitative and quantitative instruments. The study provides additional support to the theory of learning progressions called for by experts in the field. The students' learning profiles were assessed to determine the alignment with a researcher-developed learning profile. The researcher-developed learning profile incorporated the learning progressions published in the Next Generation Science Standards, as well as current research in learning progressions for 5-10th grade students studying genetics. Students were found to obtain understanding of the content in a manner that was nonlinear, even circuitous. This opposes the prevailing interpretation of learning progressions, that knowledge is ascertained in escalating levels of complexity. Learning progressions have implications in teaching sequence, assessment, education research, and policy. Tracking student understanding of other populations of students would augment the body of research and enhance generalizability.

Theoretical description of protein field effects on electronic excitations of biological chromophores.

PubMed

Varsano, Daniele; Caprasecca, Stefano; Coccia, Emanuele

2017-01-11

Photoinitiated phenomena play a crucial role in many living organisms. Plants, algae, and bacteria absorb sunlight to perform photosynthesis, and convert water and carbon dioxide into molecular oxygen and carbohydrates, thus forming the basis for life on Earth. The vision of vertebrates is accomplished in the eye by a protein called rhodopsin, which upon photon absorption performs an ultrafast isomerisation of the retinal chromophore, triggering the signal cascade. Many other biological functions start with the photoexcitation of a protein-embedded pigment, followed by complex processes comprising, for example, electron or excitation energy transfer in photosynthetic complexes. The optical properties of chromophores in living systems are strongly dependent on the interaction with the surrounding environment (nearby protein residues, membrane, water), and the complexity of such interplay is, in most cases, at the origin of the functional diversity of the photoactive proteins. The specific interactions with the environment often lead to a significant shift of the chromophore excitation energies, compared with their absorption in solution or gas phase. The investigation of the optical response of chromophores is generally not straightforward, from both experimental and theoretical standpoints; this is due to the difficulty in understanding diverse behaviours and effects, occurring at different scales, with a single technique. In particular, the role played by ab initio calculations in assisting and guiding experiments, as well as in understanding the physics of photoactive proteins, is fundamental. At the same time, owing to the large size of the systems, more approximate strategies which take into account the environmental effects on the absorption spectra are also of paramount importance. Here we review the recent advances in the first-principle description of electronic and optical properties of biological chromophores embedded in a protein environment. We show their applications on paradigmatic systems, such as the light-harvesting complexes, rhodopsin and green fluorescent protein, emphasising the theoretical frameworks which are of common use in solid state physics, and emerging as promising tools for biomolecular systems.

Theoretical description of protein field effects on electronic excitations of biological chromophores

NASA Astrophysics Data System (ADS)

Varsano, Daniele; Caprasecca, Stefano; Coccia, Emanuele

2017-01-01

Photoinitiated phenomena play a crucial role in many living organisms. Plants, algae, and bacteria absorb sunlight to perform photosynthesis, and convert water and carbon dioxide into molecular oxygen and carbohydrates, thus forming the basis for life on Earth. The vision of vertebrates is accomplished in the eye by a protein called rhodopsin, which upon photon absorption performs an ultrafast isomerisation of the retinal chromophore, triggering the signal cascade. Many other biological functions start with the photoexcitation of a protein-embedded pigment, followed by complex processes comprising, for example, electron or excitation energy transfer in photosynthetic complexes. The optical properties of chromophores in living systems are strongly dependent on the interaction with the surrounding environment (nearby protein residues, membrane, water), and the complexity of such interplay is, in most cases, at the origin of the functional diversity of the photoactive proteins. The specific interactions with the environment often lead to a significant shift of the chromophore excitation energies, compared with their absorption in solution or gas phase. The investigation of the optical response of chromophores is generally not straightforward, from both experimental and theoretical standpoints; this is due to the difficulty in understanding diverse behaviours and effects, occurring at different scales, with a single technique. In particular, the role played by ab initio calculations in assisting and guiding experiments, as well as in understanding the physics of photoactive proteins, is fundamental. At the same time, owing to the large size of the systems, more approximate strategies which take into account the environmental effects on the absorption spectra are also of paramount importance. Here we review the recent advances in the first-principle description of electronic and optical properties of biological chromophores embedded in a protein environment. We show their applications on paradigmatic systems, such as the light-harvesting complexes, rhodopsin and green fluorescent protein, emphasising the theoretical frameworks which are of common use in solid state physics, and emerging as promising tools for biomolecular systems.

A multiscale modelling approach to understand atherosclerosis formation: A patient-specific case study in the aortic bifurcation

PubMed Central

Alimohammadi, Mona; Pichardo-Almarza, Cesar; Agu, Obiekezie; Díaz-Zuccarini, Vanessa

2017-01-01

Atherogenesis, the formation of plaques in the wall of blood vessels, starts as a result of lipid accumulation (low-density lipoprotein cholesterol) in the vessel wall. Such accumulation is related to the site of endothelial mechanotransduction, the endothelial response to mechanical stimuli and haemodynamics, which determines biochemical processes regulating the vessel wall permeability. This interaction between biomechanical and biochemical phenomena is complex, spanning different biological scales and is patient-specific, requiring tools able to capture such mathematical and biological complexity in a unified framework. Mathematical models offer an elegant and efficient way of doing this, by taking into account multifactorial and multiscale processes and mechanisms, in order to capture the fundamentals of plaque formation in individual patients. In this study, a mathematical model to understand plaque and calcification locations is presented: this model provides a strong interpretability and physical meaning through a multiscale, complex index or metric (the penetration site of low-density lipoprotein cholesterol, expressed as volumetric flux). Computed tomography scans of the aortic bifurcation and iliac arteries are analysed and compared with the results of the multifactorial model. The results indicate that the model shows potential to predict the majority of the plaque locations, also not predicting regions where plaques are absent. The promising results from this case study provide a proof of concept that can be applied to a larger patient population. PMID:28427316

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

Development of the Central Dogma Concept Inventory (CDCI) Assessment Tool

PubMed Central

Newman, Dina L.; Snyder, Christopher W.; Fisk, J. Nick; Wright, L. Kate

2016-01-01

Scientific teaching requires scientifically constructed, field-tested instruments to accurately evaluate student thinking and gauge teacher effectiveness. We have developed a 23-question, multiple select–format assessment of student understanding of the essential concepts of the central dogma of molecular biology that is appropriate for all levels of undergraduate biology. Questions for the Central Dogma Concept Inventory (CDCI) tool were developed and iteratively revised based on student language and review by experts. The ability of the CDCI to discriminate between levels of understanding of the central dogma is supported by field testing (N = 54), and large-scale beta testing (N = 1733). Performance on the assessment increased with experience in biology; scores covered a broad range and showed no ceiling effect, even with senior biology majors, and pre/posttesting of a single class focused on the central dogma showed significant improvement. The multiple-select format reduces the chances of correct answers by random guessing, allows students at different levels to exhibit the extent of their knowledge, and provides deeper insight into the complexity of student thinking on each theme. To date, the CDCI is the first tool dedicated to measuring student thinking about the central dogma of molecular biology, and version 5 is ready to use. PMID:27055775

Dupuytren's: a systems biology disease

PubMed Central

2011-01-01

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule. PMID:21943049

Network Medicine: From Cellular Networks to the Human Diseasome

NASA Astrophysics Data System (ADS)

Barabasi, Albert-Laszlo

2014-03-01

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The tools of network science offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction not only enrich our understanding of complex systems, but are also essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.

Biomechanical concepts applicable to minimally invasive fracture repair in small animals.

PubMed

Chao, Peini; Lewis, Daniel D; Kowaleski, Michael P; Pozzi, Antonio

2012-09-01

Understanding the basic biomechanical principles of surgical stabilization of fractures is essential for developing an appropriate preoperative plan as well as making prudent intraoperative decisions. This article aims to provide basic biomechanical knowledge essential to the understanding of the complex interaction between the mechanics and biology of fracture healing. The type of healing and the outcome can be influenced by several mechanical factors, which depend on the interaction between bone and implant. The surgeon should understand the mechanical principles of fracture fixation and be able to choose the best type of fixation for each specific fracture. Copyright © 2012 Elsevier Inc. All rights reserved.

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

PubMed Central

Yang, Fan; Wang, Jiebiao; Pierce, Brandon L.; Chen, Lin S.

2017-01-01

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types. PMID:29021290

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis.

PubMed

Yang, Fan; Wang, Jiebiao; Pierce, Brandon L; Chen, Lin S

2017-11-01

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes ( cis -eQTLs). More research is needed to identify effects of genetic variation on distant genes ( trans -eQTLs) and understand their biological mechanisms. One common trans -eQTLs mechanism is "mediation" by a local ( cis ) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are " cis -mediators" of trans -eQTLs, including those " cis -hubs" involved in regulation of many trans -genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans -eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis -mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis -hubs and trans -eQTL regulation across tissue types. © 2017 Yang et al.; Published by Cold Spring Harbor Laboratory Press.

Heterobimetallic Complexes for Theranostic Applications.

PubMed

Fernández-Moreira, Vanesa; Gimeno, M Concepción

2018-03-07

The design of more efficient anticancer drugs requires a deeper understanding of their biodistribution and mechanism of action. Cell imaging agents could help to gain insight into biological processes and, consequently, the best strategy for attaining suitable scaffolds in which both biological and imaging properties are maximized. A new concept arises in this field that is the combination of two metal fragments as collaborative partners to provide the precise emissive properties to visualize the cell as well as the optimum cytotoxic activity to build more potent and selective chemotherapeutic agents. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Conformational Transitions in Molecular Systems

NASA Astrophysics Data System (ADS)

Bachmann, M.; Janke, W.

2008-11-01

Proteins are the "work horses" in biological systems. In almost all functions specific proteins are involved. They control molecular transport processes, stabilize the cell structure, enzymatically catalyze chemical reactions; others act as molecular motors in the complex machinery of molecular synthetization processes. Due to their significance, misfolds and malfunctions of proteins typically entail disastrous diseases, such as Alzheimer's disease and bovine spongiform encephalopathy (BSE). Therefore, the understanding of the trinity of amino acid composition, geometric structure, and biological function is one of the most essential challenges for the natural sciences. Here, we glance at conformational transitions accompanying the structure formation in protein folding processes.

Research toward Malaria Vaccines

NASA Astrophysics Data System (ADS)

Miller, Louis H.; Howard, Russell J.; Carter, Richard; Good, Michael F.; Nussenzweig, Victor; Nussenzweig, Ruth S.

1986-12-01

Malaria exacts a toll of disease to people in the Tropics that seems incomprehensible to those only familiar with medicine and human health in the developed world. The methods of molecular biology, immunology, and cell biology are now being used to develop an antimalarial vaccine. The Plasmodium parasites that cause malaria have many stages in their life cycle. Each stage is antigenically distinct and potentially could be interrupted by different vaccines. However, achieving complete protection by vaccination may require a better understanding of the complexities of B- and T-cell priming in natural infections and the development of an appropriate adjuvant for use in humans.

Marine microorganisms and global nutrient cycles

NASA Astrophysics Data System (ADS)

Arrigo, Kevin R.

2005-09-01

The way that nutrients cycle through atmospheric, terrestrial, oceanic and associated biotic reservoirs can constrain rates of biological production and help structure ecosystems on land and in the sea. On a global scale, cycling of nutrients also affects the concentration of atmospheric carbon dioxide. Because of their capacity for rapid growth, marine microorganisms are a major component of global nutrient cycles. Understanding what controls their distributions and their diverse suite of nutrient transformations is a major challenge facing contemporary biological oceanographers. What is emerging is an appreciation of the previously unknown degree of complexity within the marine microbial community.

Biohumanities: rethinking the relationship between biosciences, philosophy and history of science, and society.

PubMed

Stotz, Karola; Griffiths, Paul E

2008-03-01

We argue that philosophical and historical research can constitute a "Biohumanities" that deepens our understanding of biology itself engages in constructive "science criticism," helps formulate new "visions of biology," and facilitates "critical science communication." We illustrate these ideas with two recent "experimental philosophy" studies of the concept of the gene and of the concept of innateness conducted by ourselves and collaborators. We conclude that the complex and often troubled relations between science and society are critical to both parties, and argue that the philosophy and history of science can help to make this relationship work.

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

Kufareva, Irina; Gustavsson, Martin; Zheng, Yi

Chemokines and their cell surface G protein–coupled receptors are critical for cell migration, not only in many fundamental biological processes but also in inflammatory diseases and cancer. Recent X-ray structures of two chemokines complexed with full-length receptors provided unprecedented insight into the atomic details of chemokine recognition and receptor activation, and computational modeling informed by new experiments leverages these insights to gain understanding of many more receptor:chemokine pairs. In parallel, chemokine receptor structures with small molecules reveal the complicated and diverse structural foundations of small molecule antagonism and allostery, highlight the inherent physicochemical challenges of receptor:chemokine interfaces, and suggest novelmore » epitopes that can be exploited to overcome these challenges. The structures and models promote unique understanding of chemokine receptor biology, including the interpretation of two decades of experimental studies, and will undoubtedly assist future drug discovery endeavors.« less

Trichuris muris research revisited: a journey through time.

PubMed

Hurst, Rebecca J M; Else, Kathryn J

2013-09-01

The mouse whipworm Trichuris muris has long been used as a tractable model of human Trichuriasis. Here we look back at the history of T. muris research; from the definition of the species and determination of its life cycle, through to the complex immune responses that we study today. We highlight the key research papers that have developed our understanding of immune responses to this parasite, and reflect on how original concepts have been transformed, as our knowledge of immunology has grown. Although we have a good understanding of host–parasite interactions in the context of the underlying cellular immunology, there are still many aspects of the biology of the Trichuris parasite that remain undefined. We predict that advances in parasite biology will be key in the future development of new and improved treatments for Trichuriasis.

ARF tumor suppression in the nucleolus.

PubMed

Maggi, Leonard B; Winkeler, Crystal L; Miceli, Alexander P; Apicelli, Anthony J; Brady, Suzanne N; Kuchenreuther, Michael J; Weber, Jason D

2014-06-01

Since its discovery close to twenty years ago, the ARF tumor suppressor has played a pivotal role in the field of cancer biology. Elucidating ARF's basal physiological function in the cell has been the focal interest of numerous laboratories throughout the world for many years. Our current understanding of ARF is constantly evolving to include novel frameworks for conceptualizing the regulation of this critical tumor suppressor. As a result of this complexity, there is great need to broaden our understanding of the intricacies governing the biology of the ARF tumor suppressor. The ARF tumor suppressor is a key sensor of signals that instruct a cell to grow and proliferate and is appropriately localized in nucleoli to limit these processes. This article is part of a Special Issue entitled: Role of the Nucleolus in Human Disease. Copyright © 2014 Elsevier B.V. All rights reserved.

Metabolomics and Diabetes: Analytical and Computational Approaches

PubMed Central

Sas, Kelli M.; Karnovsky, Alla; Michailidis, George

2015-01-01

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field. PMID:25713200

Visualising associations between paired ‘omics’ data sets

PubMed Central

2012-01-01

Background Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities. Results The multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis. Conclusions Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole. Availability The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application. PMID:23148523

Macro- and microscale fluid flow systems for endothelial cell biology.

PubMed

Young, Edmond W K; Simmons, Craig A

2010-01-21

Recent advances in microfluidics have brought forth new tools for studying flow-induced effects on mammalian cells, with important applications in cardiovascular, bone and cancer biology. The plethora of microscale systems developed to date demonstrate the flexibility of microfluidic designs, and showcase advantages of the microscale that are simply not available at the macroscale. However, the majority of these systems will likely not achieve widespread use in the biological laboratory due to their complexity and lack of user-friendliness. To gain widespread acceptance in the biological research community, microfluidics engineers must understand the needs of cell biologists, while biologists must be made aware of available technology. This review provides a critical evaluation of cell culture flow (CCF) systems used to study the effects of mechanical forces on endothelial cells (ECs) in vitro. To help understand the need for various designs of CCF systems, we first briefly summarize main properties of ECs and their native environments. Basic principles of various macro- and microscale systems are described and evaluated. New opportunities are uncovered for developing technologies that have potential to both improve efficiency of experimentation as well as answer important biological questions that otherwise cannot be tackled with existing systems. Finally, we discuss some of the unresolved issues related to microfluidic cell culture, suggest possible avenues of investigation that could resolve these issues, and provide an outlook for the future of microfluidics in biological research.

Approaching the molecular origins of collective dynamics in oscillating cell populations

PubMed Central

Mehta, Pankaj; Gregor, Thomas

2011-01-01

From flocking birds, to organ generation, to swarming bacterial colonies, biological systems often exhibit collective behaviors. Here, we review recent advances in our understanding of collective dynamics in cell populations. We argue that understanding population-level oscillations requires examining the system under consideration at three different levels of complexity: at the level of isolated cells, homogenous populations, and spatially structured populations. We discuss the experimental and theoretical challenges this poses and highlight how new experimental techniques, when combined with conceptual tools adapted from physics, may help us overcome these challenges. PMID:20934869

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

PubMed Central

Liao, David; Tlsty, Thea D.

2014-01-01

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

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example. PMID:23515190

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

Using Cogenerative Dialogue to Afford the Teaching and Learning of Biology in an Urban High School

ERIC Educational Resources Information Center

Otulaja, Femi Segun

2010-01-01

The body of research work presented in this dissertation integrates critical ethnography with video and conversation analyses in order to provide ways to articulate and understand the complexities associated with social life enactment as it unfolds during cogenerative dialogues and in the science classroom as the teacher and her students engage in…

Learning about protein solubility from bacterial inclusion bodies

PubMed Central

Martínez-Alonso, Mónica; González-Montalbán, Nuria; García-Fruitós, Elena; Villaverde, Antonio

2009-01-01

The progressive solving of the conformation of aggregated proteins and the conceptual understanding of the biology of inclusion bodies in recombinant bacteria is providing exciting insights on protein folding and quality. Interestingly, newest data also show an unexpected functional and structural complexity of soluble recombinant protein species and picture the whole bacterial cell factory scenario as more intricate than formerly believed. PMID:19133126

Study to Examine Psychological Processes in Suicidal Ideation and Behavior (STEPPS)

DTIC Science & Technology

2015-05-01

diagnoses to better understand suicide; suicide is the result of a complex interplay of psychological, social and biological factors . A more...encounter frequent physiological/psychological stressors, therefore identifying suicide risk and resilience factors in military personnel is vital; so...models have been developed to aid the identification of suicide-specific individual difference factors and patterns of thinking. This program of