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].

Developments in the Tools and Methodologies of Synthetic Biology

PubMed Central

Kelwick, Richard; MacDonald, James T.; Webb, Alexander J.; Freemont, Paul

2014-01-01

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a “body of knowledge” from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community. PMID:25505788

Optimizing Nutrient Uptake in Biological Transport Networks

NASA Astrophysics Data System (ADS)

Ronellenfitsch, Henrik; Katifori, Eleni

2013-03-01

Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

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.

Informing biological design by integration of systems and synthetic biology.

PubMed

Smolke, Christina D; Silver, Pamela A

2011-03-18

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

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

Synthetic biology: programming cells for biomedical applications.

PubMed

Hörner, Maximilian; Reischmann, Nadine; Weber, Wilfried

2012-01-01

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

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.

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.

Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

EPA Science Inventory

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, ...

Collaborative Modelling of the Vascular System--Designing and Evaluating a New Learning Method for Secondary Students

ERIC Educational Resources Information Center

Haugwitz, Marion; Sandmann, Angela

2010-01-01

Understanding biological structures and functions is often difficult because of their complexity and micro-structure. For example, the vascular system is a complex and only partly visible system. Constructing models to better understand biological functions is seen as a suitable learning method. Models function as simplified versions of real…

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.

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

Illustrations of mathematical modeling in biology: epigenetics, meiosis, and an outlook.

PubMed

Richards, D; Berry, S; Howard, M

2012-01-01

In the past few years, mathematical modeling approaches in biology have begun to fulfill their promise by assisting in the dissection of complex biological systems. Here, we review two recent examples of predictive mathematical modeling in plant biology. The first involves the quantitative epigenetic silencing of the floral repressor gene FLC in Arabidopsis, mediated by a Polycomb-based system. The second involves the spatiotemporal dynamics of telomere bouquet formation in wheat-rye meiosis. Although both the biology and the modeling framework of the two systems are different, both exemplify how mathematical modeling can help to accelerate discovery of the underlying mechanisms in complex biological systems. In both cases, the models that developed were relatively minimal, including only essential features, but both nevertheless yielded fundamental insights. We also briefly review the current state of mathematical modeling in biology, difficulties inherent in its application, and its potential future development.

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.

Towards physical principles of biological evolution

NASA Astrophysics Data System (ADS)

Katsnelson, Mikhail I.; Wolf, Yuri I.; Koonin, Eugene V.

2018-03-01

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in ‘emergent phenomena’. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.

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.

EPR spectroscopy of complex biological iron-sulfur systems.

PubMed

Hagen, Wilfred R

2018-02-21

From the very first discovery of biological iron-sulfur clusters with EPR, the spectroscopy has been used to study not only purified proteins but also complex systems such as respiratory complexes, membrane particles and, later, whole cells. In recent times, the emphasis of iron-sulfur biochemistry has moved from characterization of individual proteins to the systems biology of iron-sulfur biosynthesis, regulation, degradation, and implications for human health. Although this move would suggest a blossoming of System-EPR as a specific, non-invasive monitor of Fe/S (dys)homeostasis in whole cells, a review of the literature reveals limited success possibly due to technical difficulties in adherence to EPR spectroscopic and biochemical standards. In an attempt to boost application of System-EPR the required boundary conditions and their practical applications are explicitly and comprehensively formulated.

From globally coupled maps to complex-systems biology

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

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

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

Trends in modeling Biomedical Complex Systems

PubMed Central

Milanesi, Luciano; Romano, Paolo; Castellani, Gastone; Remondini, Daniel; Liò, Petro

2009-01-01

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented. PMID:19828068

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

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.

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.

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

PubMed

Jedlicka, Peter

2017-01-01

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system's behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.

Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

PubMed Central

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

PubMed

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

An integrated cell-free metabolic platform for protein production and synthetic biology

PubMed Central

Jewett, Michael C; Calhoun, Kara A; Voloshin, Alexei; Wuu, Jessica J; Swartz, James R

2008-01-01

Cell-free systems offer a unique platform for expanding the capabilities of natural biological systems for useful purposes, i.e. synthetic biology. They reduce complexity, remove structural barriers, and do not require the maintenance of cell viability. Cell-free systems, however, have been limited by their inability to co-activate multiple biochemical networks in a single integrated platform. Here, we report the assessment of biochemical reactions in an Escherichia coli cell-free platform designed to activate natural metabolism, the Cytomim system. We reveal that central catabolism, oxidative phosphorylation, and protein synthesis can be co-activated in a single reaction system. Never before have these complex systems been shown to be simultaneously activated without living cells. The Cytomim system therefore promises to provide the metabolic foundation for diverse ab initio cell-free synthetic biology projects. In addition, we describe an improved Cytomim system with enhanced protein synthesis yields (up to 1200 mg/l in 2 h) and lower costs to facilitate production of protein therapeutics and biochemicals that are difficult to make in vivo because of their toxicity, complexity, or unusual cofactor requirements. PMID:18854819

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.

Translational Systems Biology and Voice Pathophysiology

PubMed Central

Li, Nicole Y. K.; Abbott, Katherine Verdolini; Rosen, Clark; An, Gary; Hebda, Patricia A.; Vodovotz, Yoram

2011-01-01

Objectives/Hypothesis Personalized medicine has been called upon to tailor healthcare to an individual's needs. Evidence-based medicine (EBM) has advocated using randomized clinical trials with large populations to evaluate treatment effects. However, due to large variations across patients, the results are likely not to apply to an individual patient. We suggest that a complementary, systems biology approach using computational modeling may help tackle biological complexity in order to improve ultimate patient care. The purpose of the article is: 1) to review the pros and cons of EBM, and 2) to discuss the alternative systems biology method and present its utility in clinical voice research. Study Design Tutorial Methods Literature review and discussion. Results We propose that translational systems biology can address many of the limitations of EBM pertinent to voice and other health care domains, and thus complement current health research models. In particular, recent work using mathematical modeling suggests that systems biology has the ability to quantify the highly complex biologic processes underlying voice pathophysiology. Recent data support the premise that this approach can be applied specifically in the case of phonotrauma and surgically induced vocal fold trauma, and may have particular power to address personalized medicine. Conclusions We propose that evidence around vocal health and disease be expanded beyond a population-based method to consider more fully issues of complexity and systems interactions, especially in implementing personalized medicine in voice care and beyond. PMID:20025041

A new organismal systems biology: how animals walk the tight rope between stability and change.

PubMed

Padilla, Dianna K; Tsukimura, Brian

2014-07-01

The amount of knowledge in the biological sciences is growing at an exponential rate. Simultaneously, the incorporation of new technologies in gathering scientific information has greatly accelerated our capacity to ask, and answer, new questions. How do we, as organismal biologists, meet these challenges, and develop research strategies that will allow us to address the grand challenge question: how do organisms walk the tightrope between stability and change? Organisms and organismal systems are complex, and multi-scale in both space and time. It is clear that addressing major questions about organismal biology will not come from "business as usual" approaches. Rather, we require the collaboration of a wide range of experts and integration of biological information with more quantitative approaches traditionally found in engineering and applied mathematics. Research programs designed to address grand challenge questions will require deep knowledge and expertise within subfields of organismal biology, collaboration and integration among otherwise disparate areas of research, and consideration of organisms as integrated systems. Our ability to predict which features of complex integrated systems provide the capacity to be robust in changing environments is poorly developed. A predictive organismal biology is needed, but will require more quantitative approaches than are typical in biology, including complex systems-modeling approaches common to engineering. This new organismal systems biology will have reciprocal benefits for biologists, engineers, and mathematicians who address similar questions, including those working on control theory and dynamical systems biology, and will develop the tools we need to address the grand challenge questions of the 21st century. © 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.

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

PubMed

Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Mathematical modeling of physiological systems: an essential tool for discovery.

PubMed

Glynn, Patric; Unudurthi, Sathya D; Hund, Thomas J

2014-08-28

Mathematical models are invaluable tools for understanding the relationships between components of a complex system. In the biological context, mathematical models help us understand the complex web of interrelations between various components (DNA, proteins, enzymes, signaling molecules etc.) in a biological system, gain better understanding of the system as a whole, and in turn predict its behavior in an altered state (e.g. disease). Mathematical modeling has enhanced our understanding of multiple complex biological processes like enzyme kinetics, metabolic networks, signal transduction pathways, gene regulatory networks, and electrophysiology. With recent advances in high throughput data generation methods, computational techniques and mathematical modeling have become even more central to the study of biological systems. In this review, we provide a brief history and highlight some of the important applications of modeling in biological systems with an emphasis on the study of excitable cells. We conclude with a discussion about opportunities and challenges for mathematical modeling going forward. In a larger sense, the review is designed to help answer a simple but important question that theoreticians frequently face from interested but skeptical colleagues on the experimental side: "What is the value of a model?" Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-level and hybrid modelling approaches for systems biology.

PubMed

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

2017-01-01

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

A toolbox for discrete modelling of cell signalling dynamics.

PubMed

Paterson, Yasmin Z; Shorthouse, David; Pleijzier, Markus W; Piterman, Nir; Bendtsen, Claus; Hall, Benjamin A; Fisher, Jasmin

2018-06-18

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

PubMed Central

Jedlicka, Peter

2017-01-01

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system’s behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics. PMID:29163041

Dissecting innate immune responses with the tools of systems biology.

PubMed

Smith, Kelly D; Bolouri, Hamid

2005-02-01

Systems biology strives to derive accurate predictive descriptions of complex systems such as innate immunity. The innate immune system is essential for host defense, yet the resulting inflammatory response must be tightly regulated. Current understanding indicates that this system is controlled by complex regulatory networks, which maintain homoeostasis while accurately distinguishing pathogenic infections from harmless exposures. Recent studies have used high throughput technologies and computational techniques that presage predictive models and will be the foundation of a systems level understanding of innate immunity.

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

Using Simple Manipulatives to Improve Student Comprehension of a Complex Biological Process: Protein Synthesis

ERIC Educational Resources Information Center

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…

Synthetic biology: new engineering rules for an emerging discipline

PubMed Central

Andrianantoandro, Ernesto; Basu, Subhayu; Karig, David K; Weiss, Ron

2006-01-01

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development. PMID:16738572

Synthetic biology: new engineering rules for an emerging discipline.

PubMed

Andrianantoandro, Ernesto; Basu, Subhayu; Karig, David K; Weiss, Ron

2006-01-01

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development.

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.

Network science of biological systems at different scales: A review

NASA Astrophysics Data System (ADS)

Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

2018-03-01

Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

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...

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

From the Director: The Joy of Science, the Courage of Research

MedlinePlus

... Dr. Zerhouni , one that combines an appreciation of biological complexity with the fearless search for scientific knowledge. ... techniques for greater understanding of the complexity of biological systems. The one thing that has driven my ...

Advances and Computational Tools towards Predictable Design in Biological Engineering

PubMed Central

2014-01-01

The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated. PMID:25161694

Toxicity of silver nanoparticles in biological systems: Does the complexity of biological systems matter?

PubMed

Vazquez-Muñoz, Roberto; Borrego, Belen; Juárez-Moreno, Karla; García-García, Maritza; Mota Morales, Josué D; Bogdanchikova, Nina; Huerta-Saquero, Alejandro

2017-07-05

Currently, nanomaterials are more frequently in our daily life, specifically in biomedicine, electronics, food, textiles and catalysis just to name a few. Although nanomaterials provide many benefits, recently their toxicity profiles have begun to be explored. In this work, the toxic effects of silver nanoparticles (35nm-average diameter and Polyvinyl-Pyrrolidone-coated) on biological systems of different levels of complexity was assessed in a comprehensive and comparatively way, through a variety of viability and toxicological assays. The studied organisms included viruses, bacteria, microalgae, fungi, animal and human cells (including cancer cell lines). It was found that biological systems of different taxonomical groups are inhibited at concentrations of silver nanoparticles within the same order of magnitude. Thus, the toxicity of nanomaterials on biological/living systems, constrained by their complexity, e.g. taxonomic groups, resulted contrary to the expected. The fact that cells and virus are inhibited with a concentration of silver nanoparticles within the same order of magnitude could be explained considering that silver nanoparticles affects very primitive cellular mechanisms by interacting with fundamental structures for cells and virus alike. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

I-DIRT, a general method for distinguishing between specific and nonspecific protein interactions.

PubMed

Tackett, Alan J; DeGrasse, Jeffrey A; Sekedat, Matthew D; Oeffinger, Marlene; Rout, Michael P; Chait, Brian T

2005-01-01

Isolation of protein complexes via affinity-tagged proteins provides a powerful tool for studying biological systems, but the technique is often compromised by co-enrichment of nonspecifically interacting proteins. We describe a new technique (I-DIRT) that distinguishes contaminants from bona fide interactors in immunopurifications, overcoming this most challenging problem in defining protein complexes. I-DIRT will be of broad value for studying protein complexes in biological systems that can be metabolically labeled.

Modeling biology with HDL languages: a first step toward a genetic design automation tool inspired from microelectronics.

PubMed

Gendrault, Yves; Madec, Morgan; Lallement, Christophe; Haiech, Jacques

2014-04-01

Nowadays, synthetic biology is a hot research topic. Each day, progresses are made to improve the complexity of artificial biological functions in order to tend to complex biodevices and biosystems. Up to now, these systems are handmade by bioengineers, which require strong technical skills and leads to nonreusable development. Besides, scientific fields that share the same design approach, such as microelectronics, have already overcome several issues and designers succeed in building extremely complex systems with many evolved functions. On the other hand, in systems engineering and more specifically in microelectronics, the development of the domain has been promoted by both the improvement of technological processes and electronic design automation tools. The work presented in this paper paves the way for the adaptation of microelectronics design tools to synthetic biology. Considering the similarities and differences between the synthetic biology and microelectronics, the milestones of this adaptation are described. The first one concerns the modeling of biological mechanisms. To do so, a new formalism is proposed, based on an extension of the generalized Kirchhoff laws to biology. This way, a description of all biological mechanisms can be made with languages widely used in microelectronics. Our approach is therefore successfully validated on specific examples drawn from the literature.

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.

Directed Evolution as a Powerful Synthetic Biology Tool

PubMed Central

Cobb, Ryan E.; Sun, Ning; Zhao, Huimin

2012-01-01

At the heart of synthetic biology lies the goal of rationally engineering a complete biological system to achieve a specific objective, such as bioremediation and synthesis of a valuable drug, chemical, or biofuel molecule. However, the inherent complexity of natural biological systems has heretofore precluded generalized application of this approach. Directed evolution, a process which mimics Darwinian selection on a laboratory scale, has allowed significant strides to be made in the field of synthetic biology by allowing rapid identification of desired properties from large libraries of variants. Improvement in biocatalyst activity and stability, engineering of biosynthetic pathways, tuning of functional regulatory systems and logic circuits, and development of desired complex phenotypes in industrial host organisms have all been achieved by way of directed evolution. Here, we review recent contributions of directed evolution to synthetic biology at the protein, pathway, network, and whole cell levels. PMID:22465795

Information Complexity and Biology

NASA Astrophysics Data System (ADS)

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

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

Design and control strategies for CELSS - Integrating mechanistic paradigms and biological complexities

NASA Technical Reports Server (NTRS)

Moore, B., III; Kaufmann, R.; Reinhold, C.

1981-01-01

Systems analysis and control theory consideration are given to simulations of both individual components and total systems, in order to develop a reliable control strategy for a Controlled Ecological Life Support System (CELSS) which includes complex biological components. Because of the numerous nonlinearities and tight coupling within the biological component, classical control theory may be inadequate and the statistical analysis of factorial experiments more useful. The range in control characteristics of particular species may simplify the overall task by providing an appropriate balance of stability and controllability to match species function in the overall design. The ultimate goal of this research is the coordination of biological and mechanical subsystems in order to achieve a self-supporting environment.

Efficient biological conversion of carbon monoxide (CO) to carbon dioxide (CO2) and for utilization in bioplastic production by Ralstonia eutropha through the display of an enzyme complex on the cell surface.

PubMed

Hyeon, Jeong Eun; Kim, Seung Wook; Park, Chulhwan; Han, Sung Ok

2015-06-25

An enzyme complex for biological conversion of CO to CO2 was anchored on the cell surface of the CO2-utilizing Ralstonia eutropha and successfully resulted in a 3.3-fold increase in conversion efficiency. These results suggest that this complexed system may be a promising strategy for CO2 utilization as a biological tool for the production of bioplastics.

A Framework for Understanding the Characteristics of Complexity in Biology

ERIC Educational Resources Information Center

Dauer, Joseph; Dauer, Jenny

2016-01-01

Understanding the functioning of natural systems is not easy, although there is general agreement that understanding complex systems is an important goal for science education. Defining what makes a natural system complex will assist in identifying gaps in research on student reasoning about systems. The goal of this commentary is to propose a…

[New materia medica project: synthetic biology based bioactive metabolites research in medicinal plant].

PubMed

Wang, Yong

2017-03-25

In the last decade, synthetic biology research has been gradually transited from monocellular parts or devices toward more complex multicellular systems. The emerging plant synthetic biology is regarded as the "next chapter" of synthetic biology. The complex and diverse plant metabolism as the entry point, plant synthetic biology research not only helps us understand how real life is working, but also facilitates us to learn how to design and construct more complex artificial life. Bioactive compounds innovation and large-scale production are expected to be breakthrough with the redesigned plant metabolism as well. In this review, we discuss the research progress in plant synthetic biology and propose the new materia medica project to lift the level of traditional Chinese herbal medicine research.

Synthetic biology approaches in cancer immunotherapy, genetic network engineering, and genome editing.

PubMed

Chakravarti, Deboki; Cho, Jang Hwan; Weinberg, Benjamin H; Wong, Nicole M; Wong, Wilson W

2016-04-18

Investigations into cells and their contents have provided evolving insight into the emergence of complex biological behaviors. Capitalizing on this knowledge, synthetic biology seeks to manipulate the cellular machinery towards novel purposes, extending discoveries from basic science to new applications. While these developments have demonstrated the potential of building with biological parts, the complexity of cells can pose numerous challenges. In this review, we will highlight the broad and vital role that the synthetic biology approach has played in applying fundamental biological discoveries in receptors, genetic circuits, and genome-editing systems towards translation in the fields of immunotherapy, biosensors, disease models and gene therapy. These examples are evidence of the strength of synthetic approaches, while also illustrating considerations that must be addressed when developing systems around living cells.

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.

Natural selection and self-organization in complex adaptive systems.

PubMed

Di Bernardo, Mirko

2010-01-01

The central theme of this work is self-organization "interpreted" both from the point of view of theoretical biology, and from a philosophical point of view. By analysing, on the one hand, those which are now considered--not only in the field of physics--some of the most important discoveries, that is complex systems and deterministic chaos and, on the other hand, the new frontiers of systemic biology, this work highlights how large thermodynamic systems which are open can spontaneously stay in an orderly regime. Such systems can represent the natural source of the order required for a stable self-organization, for homoeostasis and for hereditary variations. The order, emerging in enormous randomly interconnected nets of binary variables, is almost certainly only the precursor of similar orders emerging in all the varieties of complex systems. Hence, this work, by finding new foundations for the order pervading the living world, advances the daring hypothesis according to which Darwinian natural selection is not the only source of order in the biosphere. Thus, the article, by examining the passage from Prigogine's dissipative structures theory to the contemporary theory of biological complexity, highlights the development of a coherent and continuous line of research which is set to individuate the general principles marking the profound reality of that mysterious self-organization characterizing the complexity of life.

Sensitivity analysis of dynamic biological systems with time-delays.

PubMed

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

2010-10-15

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

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.

PubMed

Williams, Richard A; Timmis, Jon; Qwarnstrom, Eva E

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

PubMed Central

Timmis, Jon; Qwarnstrom, Eva E.

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model. PMID:27571414

Thinking on building the network cardiovasology of Chinese medicine.

PubMed

Yu, Gui; Wang, Jie

2012-11-01

With advances in complex network theory, the thinking and methods regarding complex systems have changed revolutionarily. Network biology and network pharmacology were built by applying network-based approaches in biomedical research. The cardiovascular system may be regarded as a complex network, and cardiovascular diseases may be taken as the damage of structure and function of the cardiovascular network. Although Chinese medicine (CM) is effective in treating cardiovascular diseases, its mechanisms are still unclear. With the guidance of complex network theory, network biology and network pharmacology, network-based approaches could be used in the study of CM in preventing and treating cardiovascular diseases. A new discipline-network cardiovasology of CM was, therefore, developed. In this paper, complex network theory, network biology and network pharmacology were introduced and the connotation of "disease-syndrome-formula-herb" was illustrated from the network angle. Network biology could be used to analyze cardiovascular diseases and syndromes and network pharmacology could be used to analyze CM formulas and herbs. The "network-network"-based approaches could provide a new view for elucidating the mechanisms of CM treatment.

On the interplay between mathematics and biology. Hallmarks toward a new systems biology

NASA Astrophysics Data System (ADS)

Bellomo, Nicola; Elaiw, Ahmed; Althiabi, Abdullah M.; Alghamdi, Mohammed Ali

2015-03-01

This paper proposes a critical analysis of the existing literature on mathematical tools developed toward systems biology approaches and, out of this overview, develops a new approach whose main features can be briefly summarized as follows: derivation of mathematical structures suitable to capture the complexity of biological, hence living, systems, modeling, by appropriate mathematical tools, Darwinian type dynamics, namely mutations followed by selection and evolution. Moreover, multiscale methods to move from genes to cells, and from cells to tissue are analyzed in view of a new systems biology approach.

"Life-bearing molecules" versus "life-embodying systems": Two contrasting views on the what-is-life (WIL) problem persisting from the early days of molecular biology to the post-genomic cell- and organism-level biology.

PubMed

Sato, Naoki

2018-05-01

"What is life?" is an ultimate biological quest for the principle that makes organisms alive. This 'WIL problem' is not, however, a simple one that we have a straightforward strategy to attack. From the beginning, molecular biology tried to identify molecules that bear the essence of life: the double helical DNA represented replication, and enzymes were micro-actuators of biological activities. A dominating idea behind these mainstream biological studies relies on the identification of life-bearing molecules, which themselves are models of life. Another, prevalent idea emphasizes that life resides in the whole system of an organism, but not in some particular molecules. The behavior of a complex system may be considered to embody the essence of life. The thermodynamic view of life system in the early 20th century was remodeled as physics of complex systems and systems biology. The two views contrast with each other, but they are no longer heritage of the historical dualism in biology, such as mechanism/materialism versus vitalism, or reductionism versus holism. These two views are both materialistic and mechanistic, and act as driving forces of modern biology. In reality, molecules function in a context of systems, whereas systems presuppose functional molecules. A key notion to reconcile this conflict is that subjects of biological studies are given before we start to study them. Cell- or organism-level biology is destined to the dialectic of molecules and systems, but this antagonism can be resolved by dynamic thinking involving biological evolution. Copyright © 2018 Elsevier B.V. All rights reserved.

The multi-replication protein A (RPA) system--a new perspective.

PubMed

Sakaguchi, Kengo; Ishibashi, Toyotaka; Uchiyama, Yukinobu; Iwabata, Kazuki

2009-02-01

Replication protein A (RPA) complex has been shown, using both in vivo and in vitro approaches, to be required for most aspects of eukaryotic DNA metabolism: replication, repair, telomere maintenance and homologous recombination. Here, we review recent data concerning the function and biological importance of the multi-RPA complex. There are distinct complexes of RPA found in the biological kingdoms, although for a long time only one type of RPA complex was believed to be present in eukaryotes. Each complex probably serves a different role. In higher plants, three distinct large and medium subunits are present, but only one species of the smallest subunit. Each of these protein subunits forms stable complexes with their respective partners. They are paralogs as complex. Humans possess two paralogs and one analog of RPA. The multi-RPA system can be regarded as universal in eukaryotes. Among eukaryotic kingdoms, paralogs, orthologs, analogs and heterologs of many DNA synthesis-related factors, including RPA, are ubiquitous. Convergent evolution seems to be ubiquitous in these processes. Using recent findings, we review the composition and biological functions of RPA complexes.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

PubMed

Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

2018-01-02

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour and identifies the sets of rate parameters of interest.

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

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.

Can We Advance Macroscopic Quantum Systems Outside the Framework of Complex Decoherence Theory?

PubMed Central

Brezinski, Mark E; Rupnick, Maria

2016-01-01

Macroscopic quantum systems (MQS) are macroscopic systems driven by quantum rather than classical mechanics, a long studied area with minimal success till recently. Harnessing the benefits of quantum mechanics on a macroscopic level would revolutionize fields ranging from telecommunication to biology, the latter focused on here for reasons discussed. Contrary to misconceptions, there are no known physical laws that prevent the development of MQS. Instead, they are generally believed universally lost in complex systems from environmental entanglements (decoherence). But we argue success is achievable MQS with decoherence compensation developed, naturally or artificially, from top-down rather current reductionist approaches. This paper advances the MQS field by a complex systems approach to decoherence. First, why complex system decoherence approaches (top-down) are needed is discussed. Specifically, complex adaptive systems (CAS) are not amenable to reductionist models (and their master equations) because of emergent behaviour, approximation failures, not accounting for quantum compensatory mechanisms, ignoring path integrals, and the subentity problem. In addition, since MQS must exist within the context of the classical world, where rapid decoherence and prolonged coherence are both needed. Nature has already demonstrated this for quantum subsystems such as photosynthesis and magnetoreception. Second, we perform a preliminary study that illustrates a top-down approach to potential MQS. In summary, reductionist arguments against MQS are not justifiable. It is more likely they are not easily detectable in large intact classical systems or have been destroyed by reductionist experimental set-ups. This complex systems decoherence approach, using top down investigations, is critical to paradigm shifts in MQS research both in biological and non-biological systems. PMID:29200743

Can We Advance Macroscopic Quantum Systems Outside the Framework of Complex Decoherence Theory?

PubMed

Brezinski, Mark E; Rupnick, Maria

2014-07-01

Macroscopic quantum systems (MQS) are macroscopic systems driven by quantum rather than classical mechanics, a long studied area with minimal success till recently. Harnessing the benefits of quantum mechanics on a macroscopic level would revolutionize fields ranging from telecommunication to biology, the latter focused on here for reasons discussed. Contrary to misconceptions, there are no known physical laws that prevent the development of MQS. Instead, they are generally believed universally lost in complex systems from environmental entanglements (decoherence). But we argue success is achievable MQS with decoherence compensation developed, naturally or artificially, from top-down rather current reductionist approaches. This paper advances the MQS field by a complex systems approach to decoherence. First, why complex system decoherence approaches (top-down) are needed is discussed. Specifically, complex adaptive systems (CAS) are not amenable to reductionist models (and their master equations) because of emergent behaviour, approximation failures, not accounting for quantum compensatory mechanisms, ignoring path integrals, and the subentity problem. In addition, since MQS must exist within the context of the classical world, where rapid decoherence and prolonged coherence are both needed. Nature has already demonstrated this for quantum subsystems such as photosynthesis and magnetoreception. Second, we perform a preliminary study that illustrates a top-down approach to potential MQS. In summary, reductionist arguments against MQS are not justifiable. It is more likely they are not easily detectable in large intact classical systems or have been destroyed by reductionist experimental set-ups. This complex systems decoherence approach, using top down investigations, is critical to paradigm shifts in MQS research both in biological and non-biological systems.

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

Metabolic systems biology: a brief primer.

PubMed

Edwards, Lindsay M

2017-05-01

In the early to mid-20th century, reductionism as a concept in biology was challenged by key thinkers, including Ludwig von Bertalanffy. He proposed that living organisms were specific examples of complex systems and, as such, they should display characteristics including hierarchical organisation and emergent behaviour. Yet the true study of complete biological systems (for example, metabolism) was not possible until technological advances that occurred 60 years later. Technology now exists that permits the measurement of complete levels of the biological hierarchy, for example the genome and transcriptome. The complexity and scale of these data require computational models for their interpretation. The combination of these - systems thinking, high-dimensional data and computation - defines systems biology, typically accompanied by some notion of iterative model refinement. Only sequencing-based technologies, however, offer full coverage. Other 'omics' platforms trade coverage for sensitivity, although the densely connected nature of biological networks suggests that full coverage may not be necessary. Systems biology models are often characterised as either 'bottom-up' (mechanistic) or 'top-down' (statistical). This distinction can mislead, as all models rely on data and all are, to some degree, 'middle-out'. Systems biology has matured as a discipline, and its methods are commonplace in many laboratories. However, many challenges remain, especially those related to large-scale data integration. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Systems genetics approaches to understand complex traits

PubMed Central

Civelek, Mete; Lusis, Aldons J.

2014-01-01

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease. PMID:24296534

The emerging genomics and systems biology research lead to systems genomics studies.

PubMed

Yang, Mary Qu; Yoshigoe, Kenji; Yang, William; Tong, Weida; Qin, Xiang; Dunker, A; Chen, Zhongxue; Arbania, Hamid R; Liu, Jun S; Niemierko, Andrzej; Yang, Jack Y

2014-01-01

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology. The Mid-South Bioinformatics Centre (MBC) and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and University of Arkansas for Medical Sciences are particularly interested in promoting education and research advancement in this prospectively emerging field. Based on past investigations and research outcomes, MBC is further utilizing differential gene and isoform/exon expression from RNA-seq and co-regulation from the ChiP-seq specific for different phenotypes in combination with protein-protein interactions, and protein-DNA interactions to construct high-level gene networks for an integrative genome-phoneme investigation at systems biology level.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2017-07-01

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Thermal Quantum Correlations in Photosynthetic Light-Harvesting Complexes

NASA Astrophysics Data System (ADS)

Mahdian, M.; Kouhestani, H.

2015-08-01

Photosynthesis is one of the ancient biological processes, playing crucial role converting solar energy to cellular usable currency. Environmental factors and external perturbations has forced nature to choose systems with the highest efficiency and performance. Recent theoretical and experimental studies have proved the presence of quantum properties in biological systems. Energy transfer systems like Fenna-Matthews-Olson (FMO) complex shows quantum entanglement between sites of Bacteriophylla molecules in protein environment and presence of decoherence. Complex biological systems implement more truthful mechanisms beside chemical-quantum correlations to assure system's efficiency. In this study we investigate thermal quantum correlations in FMO protein of the photosynthetic apparatus of green sulfur bacteria by quantum discord measure. The results confirmed existence of remarkable quantum correlations of of BChla pigments in room temperature. This results approve involvement of quantum correlation mechanisms for information storage and retention in living organisms that could be useful for further evolutionary studies. Inspired idea of this study is potentially interesting to practice by the same procedure in genetic data transfer mechanisms.

Endogenous Biologically Inspired Art of Complex Systems.

PubMed

Ji, Haru; Wakefield, Graham

2016-01-01

Since 2007, Graham Wakefield and Haru Ji have looked to nature for inspiration as they have created a series of "artificial natures," or interactive visualizations of biologically inspired complex systems that can evoke nature-like aesthetic experiences within mixed-reality art installations. This article describes how they have applied visualization, sonification, and interaction design in their work with artificial ecosystems and organisms using specific examples from their exhibited installations.

Synthetic Analog and Digital Circuits for Cellular Computation and Memory

PubMed Central

Purcell, Oliver; Lu, Timothy K.

2014-01-01

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene circuits that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation. PMID:24794536

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.

Absorption spectroscopic studies of Np(IV) complexes

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

Reed, D. T.

2004-01-01

The complexation of neptunium (IV) with selected inorganic and organic ligands was studied as part of an investigation to establish key subsurface interactions between neptunium and biological systems. The prevalence of reducing environments in most subsurface migation scenarios, which are in many cases induced by biological activity, has increased the role and importance of Np(IV) as a key subsurface neptunium oxidation state. The biodegradation of larger organics that often coexist with actinides in the subsurface leads to the formation of many organic acids as transient products that, by complexation, play a key role in defining the fate and speciation ofmore » neptunium in biologically active systems. These often compete with inorganic complexes e.g. hydrolysis and phosphate. Herein we report the results of a series of complexation studies based on new band formation of the characteristic 960 nm band for Np(IV). Formation constants for Np(IV) complexes with phosphate, hydrolysis, succinate, acetohydroxamic acid, and acetate were determined. These results show the 960 nm absorption band to be very amenable to these types of complexation studies.« less

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…

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.

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

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

On the interplay between mathematics and biology: hallmarks toward a new systems biology.

PubMed

Bellomo, Nicola; Elaiw, Ahmed; Althiabi, Abdullah M; Alghamdi, Mohammed Ali

2015-03-01

This paper proposes a critical analysis of the existing literature on mathematical tools developed toward systems biology approaches and, out of this overview, develops a new approach whose main features can be briefly summarized as follows: derivation of mathematical structures suitable to capture the complexity of biological, hence living, systems, modeling, by appropriate mathematical tools, Darwinian type dynamics, namely mutations followed by selection and evolution. Moreover, multiscale methods to move from genes to cells, and from cells to tissue are analyzed in view of a new systems biology approach. Copyright © 2014 Elsevier B.V. All rights reserved.

Is Self-organization a Rational Expectation?

NASA Astrophysics Data System (ADS)

Luediger, Heinz

Over decades and under varying names the study of biology-inspired algorithms applied to non-living systems has been the subject of a small and somewhat exotic research community. Only the recent coincidence of a growing inability to master the design, development and operation of increasingly intertwined systems and processes, and an accelerated trend towards a naïve if not romanticizing view of nature in the sciences, has led to the adoption of biology-inspired algorithmic research by a wider range of sciences. Adaptive systems, as we apparently observe in nature, are meanwhile viewed as a promising way out of the complexity trap and, propelled by a long list of ‘self’ catchwords, complexity research has become an influential stream in the science community. This paper presents four provocative theses that cast doubt on the strategic potential of complexity research and the viability of large scale deployment of biology-inspired algorithms in an expectation driven world.

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.

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

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.

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

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.

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

PubMed

Astakhov, Vadim

2009-01-01

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

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

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

Universal principles underlying network science, and their ever-increasing applications in biomedicine, underscore the unprecedented capacity of systems biology based strategies to synthesize and resolve massive high throughput generated datasets. Enabling previously unattainable comprehension of biological complexity, systems approaches have accelerated progress in elucidating disease prediction, progression, and outcome. Applied to the spectrum of states spanning health and disease, network proteomics establishes a collation, integration, and prioritization algorithm to guide mapping and decoding of proteome landscapes from large-scale raw data. Providing unparalleled deconvolution of protein lists into global interactomes, integrative systems proteomics enables objective, multi-modal interpretation at molecular, pathway, and network scales, merging individual molecular components, their plurality of interactions, and functional contributions for systems comprehension. As such, network systems approaches are increasingly exploited for objective interpretation of cardiovascular proteomics studies. Here, we highlight network systems proteomic analysis pipelines for integration and biological interpretation through protein cartography, ontological categorization, pathway and functional enrichment and complex network analysis. PMID:22896016

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

CORUM: the comprehensive resource of mammalian protein complexes

PubMed Central

Ruepp, Andreas; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Frishman, Goar; Montrone, Corinna; Stransky, Michael; Waegele, Brigitte; Schmidt, Thorsten; Doudieu, Octave Noubibou; Stümpflen, Volker; Mewes, H. Werner

2008-01-01

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The CORUM (http://mips.gsf.de/genre/proj/corum/index.html) database is a collection of experimentally verified mammalian protein complexes. Information is manually derived by critical reading of the scientific literature from expert annotators. Information about protein complexes includes protein complex names, subunits, literature references as well as the function of the complexes. For functional annotation, we use the FunCat catalogue that enables to organize the protein complex space into biologically meaningful subsets. The database contains more than 1750 protein complexes that are built from 2400 different genes, thus representing 12% of the protein-coding genes in human. A web-based system is available to query, view and download the data. CORUM provides a comprehensive dataset of protein complexes for discoveries in systems biology, analyses of protein networks and protein complex-associated diseases. Comparable to the MIPS reference dataset of protein complexes from yeast, CORUM intends to serve as a reference for mammalian protein complexes. PMID:17965090

Review Article: Shallow Draughts--Larsen-Freeman and Cameron on Complexity

ERIC Educational Resources Information Center

Gregg, Kevin R.

2010-01-01

Complexity theory is a field of physics that studies the nature and behavior of complex systems, systems whose elements interact in complex and unpredictable ways. Recent years have seen a number of attempts to extend its scope to the biological and social sciences, and now Larsen-Freeman and Cameron offer a view of applied linguistics from a…

Reflecting on complexity of biological systems: Kant and beyond?

PubMed

Van de Vijver, Gertrudis; Van Speybroeck, Linda; Vandevyvere, Windy

2003-01-01

Living organisms are currently most often seen as complex dynamical systems that develop and evolve in relation to complex environments. Reflections on the meaning of the complex dynamical nature of living systems show an overwhelming multiplicity in approaches, descriptions, definitions and methodologies. Instead of sustaining an epistemic pluralism, which often functions as a philosophical armistice in which tolerance and so-called neutrality discharge proponents of the burden to clarify the sources and conditions of agreement and disagreement, this paper aims at analysing: (i) what has been Kant's original conceptualisation of living organisms as natural purposes; (ii) how the current perspectives are to be related to Kant's viewpoint; (iii) what are the main trends in current complexity thinking. One of the basic ideas is that the attention for structure and its epistemological consequences witness to a great extent of Kant's viewpoint, and that the idea of organisational stratification today constitutes a different breeding ground within which complexity issues are raised. The various approaches of complexity in biological systems are captured in terms of two different styles, universalism and (weak and strong) constructivism, between which hybrid forms exist.

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

Discovery of a new method for potent drug development using power function of stoichiometry ofhomomeric biocomplexes or biological nanomotors

PubMed Central

Pi, Fengmei; Vieweger, Mario; Zhao, Zhengyi; Wang, Shaoying; Guo, Peixuan

2015-01-01

Introduction Multidrug resistance and the appearance of incurable diseases inspire the quest for potent therapeutics. Areas Covered We review a new methodology in designing potent drugs by targeting multi-subunit homomeric biological motors, machines, or complexes with Z>1 and K=1, where Z is the stoichiometry of the target, and K is the number of drugged subunits required to block the function of the complex. The condition is similar to a series, electrical circuit of Christmas decorations; failure of one light bulb causes the entire lighting system to lose power. In most multisubunit, homomeric biological systems, a sequential coordination or cooperative action mechanism is utilized, thus K equals 1. Drug inhibition depends on the ratio of drugged to nondrugged complexes. When K=1, and Z>1, the inhibition effect follows a power law with respect to Z, leading to enhanced drug potency. The hypothesis that the potency of drug inhibition depends on the stoichiometry of the targeted biological complexes was recently quantified by Yang-Hui's Triangle (or binomial distribution), and proved using a highly sensitive in vitro phi29 viral DNA packaging system. Examples of targeting homomeric bio-complexes with high stoichiometry for potent drug discovery are discussed. Expert Opinion Biomotors with multiple subunits are widespread in viruses, bacteria, and cells, making this approach generally applicable in the development of inhibition drugs with high efficiency. PMID:26307193

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

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.

Synthetic analog and digital circuits for cellular computation and memory.

PubMed

Purcell, Oliver; Lu, Timothy K

2014-10-01

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene networks that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. 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.

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.

Systems Modeling in Developmental Toxicity

EPA Science Inventory

An individual starts off as a single cell, the progeny of which form complex structures that are themselves integrated into progressively larger systems. Developmental biology is concerned with how this cellular complexity and patterning arises through orchestration of cell divi...

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

Apparatus and Methods for Manipulation and Optimization of Biological Systems

NASA Technical Reports Server (NTRS)

Sun, Ren (Inventor); Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Yu, Fuqu (Inventor)

2014-01-01

The invention provides systems and methods for manipulating biological systems, for example to elicit a more desired biological response from a biological sample, such as a tissue, organ, and/or a cell. In one aspect, the invention operates by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The invention can be used, e.g., to optimize any biological system, e.g., bioreactors for proteins, and the like, small molecules, polysaccharides, lipids, and the like. Another use of the apparatus and methods includes is for the discovery of key parameters in complex biological systems.

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

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

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

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

2018-05-24

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

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

Characterising the Development of the Understanding of Human Body Systems in High-School Biology Students--A Longitudinal Study

ERIC Educational Resources Information Center

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

2017-01-01

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…

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.

Maps, books and other metaphors for systems biology.

PubMed

Ouzounis, Christos; Mazière, Pierre

2006-07-01

We briefly review the use of metaphors in science and progressively focus on fields from biology and molecular biology to genomics and bioinformatics. We discuss how metaphors are both a tool for scientific exploration and a medium for public communication of complex subjects, by various short examples. Finally, we propose a metaphor for systems biology that provides an illuminating perspective for the ambitious goals of this field and delimits its current agenda.

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.

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

PubMed Central

Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Language Networks as Complex Systems

ERIC Educational Resources Information Center

Lee, Max Kueiming; Ou, Sheue-Jen

2008-01-01

Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

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.

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.

The semiotics of control and modeling relations in complex systems.

PubMed

Joslyn, C

2001-01-01

We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but differing topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments, respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.

A program code generator for multiphysics biological simulation using markup languages.

PubMed

Amano, Akira; Kawabata, Masanari; Yamashita, Yoshiharu; Rusty Punzalan, Florencio; Shimayoshi, Takao; Kuwabara, Hiroaki; Kunieda, Yoshitoshi

2012-01-01

To cope with the complexity of the biological function simulation models, model representation with description language is becoming popular. However, simulation software itself becomes complex in these environment, thus, it is difficult to modify the simulation conditions, target computation resources or calculation methods. In the complex biological function simulation software, there are 1) model equations, 2) boundary conditions and 3) calculation schemes. Use of description model file is useful for first point and partly second point, however, third point is difficult to handle for various calculation schemes which is required for simulation models constructed from two or more elementary models. We introduce a simulation software generation system which use description language based description of coupling calculation scheme together with cell model description file. By using this software, we can easily generate biological simulation code with variety of coupling calculation schemes. To show the efficiency of our system, example of coupling calculation scheme with three elementary models are shown.

“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

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.

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

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

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

Biocellion: accelerating computer simulation of multicellular biological system models.

PubMed

Kang, Seunghwa; Kahan, Simon; McDermott, Jason; Flann, Nicholas; Shmulevich, Ilya

2014-11-01

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. 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. 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. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

Engineering scalable biological systems

PubMed Central

2010-01-01

Synthetic biology is focused on engineering biological organisms to study natural systems and to provide new solutions for pressing medical, industrial and environmental problems. At the core of engineered organisms are synthetic biological circuits that execute the tasks of sensing inputs, processing logic and performing output functions. In the last decade, significant progress has been made in developing basic designs for a wide range of biological circuits in bacteria, yeast and mammalian systems. However, significant challenges in the construction, probing, modulation and debugging of synthetic biological systems must be addressed in order to achieve scalable higher-complexity biological circuits. Furthermore, concomitant efforts to evaluate the safety and biocontainment of engineered organisms and address public and regulatory concerns will be necessary to ensure that technological advances are translated into real-world solutions. PMID:21468204

Sender–receiver systems and applying information theory for quantitative synthetic biology

PubMed Central

Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

2015-01-01

Sender–receiver (S–R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S–R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

Nestedness across biological scales

PubMed Central

Marquitti, Flavia M. D.; Raimundo, Rafael L. G.; Sebastián-González, Esther; Coltri, Patricia P.; Perez, S. Ivan; Brandt, Débora Y. C.; Nunes, Kelly; Daura-Jorge, Fábio G.; Floeter, Sergio R.; Guimarães, Paulo R.

2017-01-01

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks. PMID:28166284

Automated and miniaturized detection of biological threats with a centrifugal microfluidic system

NASA Astrophysics Data System (ADS)

Mark, D.; van Oordt, T.; Strohmeier, O.; Roth, G.; Drexler, J.; Eberhard, M.; Niedrig, M.; Patel, P.; Zgaga-Griesz, A.; Bessler, W.; Weidmann, M.; Hufert, F.; Zengerle, R.; von Stetten, F.

2012-06-01

The world's growing mobility, mass tourism, and the threat of terrorism increase the risk of the fast spread of infectious microorganisms and toxins. Today's procedures for pathogen detection involve complex stationary devices, and are often too time consuming for a rapid and effective response. Therefore a robust and mobile diagnostic system is required. We present a microstructured LabDisk which performs complex biochemical analyses together with a mobile centrifugal microfluidic device which processes the LabDisk. This portable system will allow fully automated and rapid detection of biological threats at the point-of-need.

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

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

PubMed Central

Koorehdavoudi, Hana; Bogdan, Paul

2016-01-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity. PMID:27297496

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

NASA Astrophysics Data System (ADS)

Koorehdavoudi, Hana; Bogdan, Paul

2016-06-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity.

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.

Petri net modelling of biological networks.

PubMed

Chaouiya, Claudine

2007-07-01

Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.

Metabolic Compartmentation – A System Level Property of Muscle Cells

PubMed Central

Saks, Valdur; Beraud, Nathalie; Wallimann, Theo

2008-01-01

Problems of quantitative investigation of intracellular diffusion and compartmentation of metabolites are analyzed. Principal controversies in recently published analyses of these problems for the living cells are discussed. It is shown that the formal theoretical analysis of diffusion of metabolites based on Fick's equation and using fixed diffusion coefficients for diluted homogenous aqueous solutions, but applied for biological systems in vivo without any comparison with experimental results, may lead to misleading conclusions, which are contradictory to most biological observations. However, if the same theoretical methods are used for analysis of actual experimental data, the apparent diffusion constants obtained are orders of magnitude lower than those in diluted aqueous solutions. Thus, it can be concluded that local restrictions of diffusion of metabolites in a cell are a system-level properties caused by complex structural organization of the cells, macromolecular crowding, cytoskeletal networks and organization of metabolic pathways into multienzyme complexes and metabolons. This results in microcompartmentation of metabolites, their channeling between enzymes and in modular organization of cellular metabolic networks. The perspectives of further studies of these complex intracellular interactions in the framework of Systems Biology are discussed. PMID:19325782

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.

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering.

PubMed

Teixeira, Ana P; Carinhas, Nuno; Dias, João M L; Cruz, Pedro; Alves, Paula M; Carrondo, Manuel J T; Oliveira, Rui

2007-12-01

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.

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

PubMed

Weber, Bruce H

2011-03-01

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

High performance computing in biology: multimillion atom simulations of nanoscale systems

PubMed Central

Sanbonmatsu, K. Y.; Tung, C.-S.

2007-01-01

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nanoscale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail. PMID:17187988

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.

A probabilistic framework for identifying biosignatures using Pathway Complexity

NASA Astrophysics Data System (ADS)

Marshall, Stuart M.; Murray, Alastair R. G.; Cronin, Leroy

2017-11-01

One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes-whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call `Pathway Complexity', that allows us not only to threshold the abiotic-biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue 'Reconceptualizing the origins of life'.

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.

Empirical modeling for intelligent, real-time manufacture control

NASA Technical Reports Server (NTRS)

Xu, Xiaoshu

1994-01-01

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

6th Institute for Systems Biology International Symposium: Systems Biology and the Environment

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

Galitski, Timothy P.

2007-04-23

Systems biology recognizes the complex multi-scale organization of biological systems, from molecules to ecosystems. The International Symposium on Systems Biology is an annual two-day event gathering the most influential researchers transforming biology into an integrative discipline investigating complex systems. In recognition of the fundamental similarity between the scientific problems addressed in environmental science and systems biology studies at the molecular, cellular, and organismal levels, the 2007 Symposium featured global leaders in “Systems Biology and the Environment.” The objective of the 2007 “Systems Biology and the Environment” International Symposium was to stimulate interdisciplinary thinking and research that spans systems biology andmore » environmental science. This Symposium was well aligned with the DOE’s Genomics: GTL program efforts to achieve scientific objectives for each of the three DOE missions: Develop biofuels as a major secure energy source for this century; Develop biological solutions for intractable environmental problems; Understand biosystems’ climate impacts and assess sequestration strategies. Our scientific program highlighted world-class research exemplifying these priorities. The Symposium featured 45 minute lectures from 12 researchers including: Penny/Sallie Chisholm of MIT gave the keynote address “Tiny Cells, Global Impact: What Prochlorococcus Can Teach Us About Systems Biology”, plus Jim Fredrickson of PNNL, Nitin Baliga of ISB, Steve Briggs of UCSD, David Cox of Perlegen Sciences, Antoine Danchin of Institut Pasteur, John Delaney of the U of Washington, John Groopman of Johns Hopkins, Ben Kerr of the U of Washington, Steve Koonin of BP, Elliott Meyerowitz of Caltech, and Ed Rubin of LBNL. The 2007 Symposium promoted DOE’s three mission areas among scientists from multiple disciplines representing academia, non-profit research institutions, and the private sector. As in all previous Symposia, we had excellent attendance of participants representing 20-30 academic or research-oriented facilities along with 25-30 private corporations from 5-10 countries. To broaden the audience for the Symposium and ensure the continued accessibility of the presentations, we made the presentation videos available afterward on the ISB’s website.« less

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.

Reverse Ecology: from systems to environments and back.

PubMed

Levy, Roie; Borenstein, Elhanan

2012-01-01

The structure of complex biological systems reflects not only their function but also the environments in which they evolved and are adapted to. Reverse Ecology-an emerging new frontier in Evolutionary Systems Biology-aims to extract this information and to obtain novel insights into an organism's ecology. The Reverse Ecology framework facilitates the translation of high-throughput genomic data into large-scale ecological data, and has the potential to transform ecology into a high-throughput field. In this chapter, we describe some of the pioneering work in Reverse Ecology, demonstrating how system-level analysis of complex biological networks can be used to predict the natural habitats of poorly characterized microbial species, their interactions with other species, and universal patterns governing the adaptation of organisms to their environments. We further present several studies that applied Reverse Ecology to elucidate various aspects of microbial ecology, and lay out exciting future directions and potential future applications in biotechnology, biomedicine, and ecological engineering.

Application of Smart Infrastructure Systems approach to precision medicine.

PubMed

Govindaraju, Diddahally R; Annaswamy, Anuradha M

2015-12-01

All biological variation is hierarchically organized dynamic network system of genomic components, organelles, cells, tissues, organs, individuals, families, populations and metapopulations. Individuals are axial in this hierarchy, as they represent antecedent, attendant and anticipated aspects of health, disease, evolution and medical care. Humans show individual specific genetic and clinical features such as complexity, cooperation, resilience, robustness, vulnerability, self-organization, latent and emergent behavior during their development, growth and senescence. Accurate collection, measurement, organization and analyses of individual specific data, embedded at all stratified levels of biological, demographic and cultural diversity - the big data - is necessary to make informed decisions on health, disease and longevity; which is a central theme of precision medicine initiative (PMI). This initiative also calls for the development of novel analytical approaches to handle complex multidimensional data. Here we suggest the application of Smart Infrastructure Systems (SIS) approach to accomplish some of the goals set forth by the PMI on the premise that biological systems and the SIS share many common features. The latter has been successfully employed in managing complex networks of non-linear adaptive controls, commonly encountered in smart engineering systems. We highlight their concordance and discuss the utility of the SIS approach in precision medicine programs.

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

Accomplishment Summary 1968-1969. Biological Computer Laboratory.

ERIC Educational Resources Information Center

Von Foerster, Heinz; And Others

This report summarizes theoretical, applied, and experimental studies in the areas of computational principles in complex intelligent systems, cybernetics, multivalued logic, and the mechanization of cognitive processes. This work is summarized under the following topic headings: properties of complex dynamic systems; computers and the language…

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.

A versatile petri net based architecture for modeling and simulation of complex biological processes.

PubMed

Nagasaki, Masao; Doi, Atsushi; Matsuno, Hiroshi; Miyano, Satoru

2004-01-01

The research on modeling and simulation of complex biological systems is getting more important in Systems Biology. In this respect, we have developed Hybrid Function Petri net (HFPN) that was newly developed from existing Petri net because of their intuitive graphical representation and their capabilities for mathematical analyses. However, in the process of modeling metabolic, gene regulatory or signal transduction pathways with the architecture, we have realized three extensions of HFPN, (i) an entity should be extended to contain more than one value, (ii) an entity should be extended to handle other primitive types, e.g. boolean, string, (iii) an entity should be extended to handle more advanced type called object that consists of variables and methods, are necessary for modeling biological systems with Petri net based architecture. To deal with it, we define a new enhanced Petri net called hybrid functional Petri net with extension (HFPNe). To demonstrate the effectiveness of the enhancements, we model and simulate with HFPNe four biological processes that are diffcult to represent with the previous architecture HFPN.

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.

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.

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

PubMed

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

2015-05-01

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

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.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

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.

Simulation of physiological systems in order to evaluate and predict the human condition in a space flight

NASA Technical Reports Server (NTRS)

Verigo, V. V.

1979-01-01

Simulation models were used to study theoretical problems of space biology and medicine. The reaction and adaptation of the main physiological systems to the complex effects of space flight were investigated. Mathematical models were discussed in terms of their significance in the selection of the structure and design of biological life support systems.

Quantification of Degeneracy in Biological Systems for Characterization of Functional Interactions Between Modules

PubMed Central

Li, Yao; Dwivedi, Gaurav; Huang, Wen; Yi, Yingfei

2012-01-01

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components. PMID:22619750

Knowledge-making distinctions in synthetic biology.

PubMed

O'Malley, Maureen A; Powell, Alexander; Davies, Jonathan F; Calvert, Jane

2008-01-01

Synthetic biology is an increasingly high-profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA-based device construction, genome-driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge-making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems. (c) 2007 Wiley Periodicals, Inc.

Phase from defocus

NASA Astrophysics Data System (ADS)

Mandula, Ondrej; Allier, Cédric; Hervé, Lionel; Denarier, Eric; Fourest-Lieuvin, Anne; Gory-Fauré, Sylvie; Vinit, Angélique; Morales, Sophie

2018-02-01

We present a simple and compact phase imaging microscope for long-term observation of non-absorbing biological samples such as unstained cells in nutritive media. The phase image is obtained from a single defocused image taken with a standard wide-field microscope. Using a semi-coherent light source allows us to computationally re-focus image post-acquisition and recover both phase and transmission of the complex specimen. The simplicity of the system reduces both the cost and its physical size and allows a long-term observation of samples directly in a standard biological incubator. The low cost of the system can contribute to the democratization of science by allowing to perform complex long-term biological experiments to the laboratories with constrained budget. In this proceeding we present several results taken with our prototype and discuss the possibilities and limitations of our system.

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.

Systems Biology-Based Investigation of Host-Plasmodium Interactions.

PubMed

Smith, Maren L; Styczynski, Mark P

2018-05-18

Malaria is a serious, complex disease caused by parasites of the genus Plasmodium. Plasmodium parasites affect multiple tissues as they evade immune responses, replicate, sexually reproduce, and transmit between vertebrate and invertebrate hosts. The explosion of omics technologies has enabled large-scale collection of Plasmodium infection data, revealing systems-scale patterns, mechanisms of pathogenesis, and the ways that host and pathogen affect each other. Here, we provide an overview of recent efforts using systems biology approaches to study host-Plasmodium interactions and the biological themes that have emerged from these efforts. We discuss some of the challenges in using systems biology for this goal, key research efforts needed to address those issues, and promising future malaria applications of systems biology. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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.

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

Complex and unexpected dynamics in simple genetic regulatory networks

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

End-Directedness and Context in Nonliving Dissipative Systems

NASA Astrophysics Data System (ADS)

Dixon, James A.; Kay, Bruce A.; Davis, Tehran J.; Kondepudi, Dilip

Biological organisms are distinguished from non-living systems, in part, by their ability to choose and strive towards particular ends. This end-directed behavior is seen across all five biological kingdoms, from single-celled organisms to the most advanced primates. The ubiquitous nature of end-directedness, across such a wide variety of biological entities, suggests that a deeper principle may be at work. We propose that end-directedness, rather than being a special ability of living systems, is actually a fundamental property of a larger class of physical systems, called dissipative structures, which are formed and maintained by the flow of energy and matter. Our work shows that dissipative structures "behave so as to persist", seeking states that increase their rate of entropy production, and thus facilitate their own persistence. In addition, we suggest that biological entities create their exquisite sensitivity to context by interweaving this fundamental end-directedness with the contextual and physical constraints of their environments. The result is a repertoire of complex behavior. We provide an example of such complex behavior emerging from contextual and physical constraints coupled with end-directedness.

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

Consistent visualizations of changing knowledge

PubMed Central

Tipney, Hannah J.; Schuyler, Ronald P.; Hunter, Lawrence

2009-01-01

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change. PMID:21347184

How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research.

PubMed

Hoskinson, A-M; Caballero, M D; Knight, J K

2013-06-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research.

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.

Investigating cholesterol metabolism and ageing using a systems biology approach.

PubMed

Morgan, A E; Mooney, K M; Wilkinson, S J; Pickles, N A; Mc Auley, M T

2017-08-01

CVD accounted for 27 % of all deaths in the UK in 2014, and was responsible for 1·7 million hospital admissions in 2013/2014. This condition becomes increasingly prevalent with age, affecting 34·1 and 29·8 % of males and females over 75 years of age respectively in 2011. The dysregulation of cholesterol metabolism with age, often observed as a rise in LDL-cholesterol, has been associated with the pathogenesis of CVD. To compound this problem, it is estimated by 2050, 22 % of the world's population will be over 60 years of age, in culmination with a growing resistance and intolerance to pre-existing cholesterol regulating drugs such as statins. Therefore, it is apparent research into additional therapies for hypercholesterolaemia and CVD prevention is a growing necessity. However, it is also imperative to recognise this complex biological system cannot be studied using a reductionist approach; rather its biological uniqueness necessitates a more integrated methodology, such as that offered by systems biology. In this review, we firstly discuss cholesterol metabolism and how it is affected by diet and the ageing process. Next, we describe therapeutic strategies for hypercholesterolaemia, and finally how the systems biology paradigm can be utilised to investigate how ageing interacts with complex systems such as cholesterol metabolism. We conclude by emphasising the need for nutritionists to work in parallel with the systems biology community, to develop novel approaches to studying cholesterol metabolism and its interaction with ageing.

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

PubMed Central

Saks, Valdur; Monge, Claire; Guzun, Rita

2009-01-01

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

Controllability and observability of Boolean networks arising from biology

NASA Astrophysics Data System (ADS)

Li, Rui; Yang, Meng; Chu, Tianguang

2015-02-01

Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

Interdisciplinary education - a predator-prey model for developing a skill set in mathematics, biology and technology

NASA Astrophysics Data System (ADS)

van der Hoff, Quay

2017-08-01

The science of biology has been transforming dramatically and so the need for a stronger mathematical background for biology students has increased. Biological students reaching the senior or post-graduate level often come to realize that their mathematical background is insufficient. Similarly, students in a mathematics programme, interested in biological phenomena, find it difficult to master the complex systems encountered in biology. In short, the biologists do not have enough mathematics and the mathematicians are not being taught enough biology. The need for interdisciplinary curricula that includes disciplines such as biology, physical science, and mathematics is widely recognized, but has not been widely implemented. In this paper, it is suggested that students develop a skill set of ecology, mathematics and technology to encourage working across disciplinary boundaries. To illustrate such a skill set, a predator-prey model that contains self-limiting factors for both predator and prey is suggested. The general idea of dynamics, is introduced and students are encouraged to discover the applicability of this approach to more complex biological systems. The level of mathematics and technology required is not advanced; therefore, it is ideal for inclusion in a senior-level or introductory graduate-level course for students interested in mathematical biology.

An integrative approach to inferring biologically meaningful gene modules.

PubMed

Cho, Ji-Hoon; Wang, Kai; Galas, David J

2011-07-26

The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

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.

Transdisciplinary Application of Cross-Scale Resilience ...

EPA Pesticide Factsheets

The cross-scale resilience model was developed in ecology to explain the emergence of resilience from the distribution of ecological functions within and across scales, and as a tool to assess resilience. We propose that the model and the underlyingdiscontinuity hypothesis are relevant to other complex adaptive systems, and can be used to identify and track changes in system parameters related to resilience. We explain the theory behind the cross-scale resilience model, review the cases where it has been applied to non-ecological systems, and discuss some examples of social-ecological, archaeological/anthropological, and economic systems where a cross-scale resilience analysis could add a quantitative dimension to our current understanding of system dynamics and resilience. We argue that the scaling and diversity parameters suitable for a resilience analysis of ecological systems are appropriate for a broad suite of systems where non-normative quantitative assessments of resilience are desired. Our planet is currently characterized by fast environmental and social change, and the cross-scale resilience model has the potential to quantify resilience across many types of complex adaptive systems. Comparative analyses of complex systems have, in fact, demonstrated commonalities among distinctly different types of systems (Schneider & Kay 1994; Holling 2001; Lansing 2003; Foster 2005; Bullmore et al. 2009). Both biological and non-biological complex systems appear t

[Progress in synthetic biology of "973 Funding Program" in China].

PubMed

Chen, Guoqiang; Wang, Ying

2015-06-01

This paper reviews progresses made in China from 2011 in areas of "Synthetic Biology" supported by State Basic Research 973 Program. Till the end of 2014, 9 "synthetic biology" projects have been initiated with emphasis on "microbial manufactures" with the 973 Funding Program. Combined with the very recent launch of one project on "mammalian cell synthetic biology" and another on "plant synthetic biology", Chinese "synthetic biology" research reflects its focus on "manufactures" while not giving up efforts on "synthetic biology" of complex systems.

Compressed learning and its applications to subcellular localization.

PubMed

Zheng, Zhong-Long; Guo, Li; Jia, Jiong; Xie, Chen-Mao; Zeng, Wen-Cai; Yang, Jie

2011-09-01

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Synthetic biology: advancing the design of diverse genetic systems

PubMed Central

Wang, Yen-Hsiang; Wei, Kathy Y.; Smolke, Christina D.

2013-01-01

A main objective of synthetic biology is to make the process of designing genetically-encoded biological systems more systematic, predictable, robust, scalable, and efficient. The examples of genetic systems in the field vary widely in terms of operating hosts, compositional approaches, and network complexity, ranging from a simple genetic switch to search-and-destroy systems. While significant advances in synthesis capabilities support the potential for the implementation of pathway- and genome-scale programs, several design challenges currently restrict the scale of systems that can be reasonably designed and implemented. Synthetic biology offers much promise in developing systems to address challenges faced in manufacturing, the environment and sustainability, and health and medicine, but the realization of this potential is currently limited by the diversity of available parts and effective design frameworks. As researchers make progress in bridging this design gap, advances in the field hint at ever more diverse applications for biological systems. PMID:23413816

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.

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.

Information and material flows in complex networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Armbruster, Dieter; Mikhailov, Alexander S.; Lefeber, Erjen

2006-04-01

In this special issue, an overview of the Thematic Institute (TI) on Information and Material Flows in Complex Systems is given. The TI was carried out within EXYSTENCE, the first EU Network of Excellence in the area of complex systems. Its motivation, research approach and subjects are presented here. Among the various methods used are many-particle and statistical physics, nonlinear dynamics, as well as complex systems, network and control theory. The contributions are relevant for complex systems as diverse as vehicle and data traffic in networks, logistics, production, and material flows in biological systems. The key disciplines involved are socio-, econo-, traffic- and bio-physics, and a new research area that could be called “biologistics”.

Conceptual Modeling in Systems Biology Fosters Empirical Findings: The mRNA Lifecycle

PubMed Central

Dori, Dov; Choder, Mordechai

2007-01-01

One of the main obstacles to understanding complex biological systems is the extent and rapid evolution of information, way beyond the capacity individuals to manage and comprehend. Current modeling approaches and tools lack adequate capacity to model concurrently structure and behavior of biological systems. Here we propose Object-Process Methodology (OPM), a holistic conceptual modeling paradigm, as a means to model both diagrammatically and textually biological systems formally and intuitively at any desired number of levels of detail. OPM combines objects, e.g., proteins, and processes, e.g., transcription, in a way that is simple and easily comprehensible to researchers and scholars. As a case in point, we modeled the yeast mRNA lifecycle. The mRNA lifecycle involves mRNA synthesis in the nucleus, mRNA transport to the cytoplasm, and its subsequent translation and degradation therein. Recent studies have identified specific cytoplasmic foci, termed processing bodies that contain large complexes of mRNAs and decay factors. Our OPM model of this cellular subsystem, presented here, led to the discovery of a new constituent of these complexes, the translation termination factor eRF3. Association of eRF3 with processing bodies is observed after a long-term starvation period. We suggest that OPM can eventually serve as a comprehensive evolvable model of the entire living cell system. The model would serve as a research and communication platform, highlighting unknown and uncertain aspects that can be addressed empirically and updated consequently while maintaining consistency. PMID:17849002

PeTTSy: a computational tool for perturbation analysis of complex systems biology models.

PubMed

Domijan, Mirela; Brown, Paul E; Shulgin, Boris V; Rand, David A

2016-03-10

Over the last decade sensitivity analysis techniques have been shown to be very useful to analyse complex and high dimensional Systems Biology models. However, many of the currently available toolboxes have either used parameter sampling, been focused on a restricted set of model observables of interest, studied optimisation of a objective function, or have not dealt with multiple simultaneous model parameter changes where the changes can be permanent or temporary. Here we introduce our new, freely downloadable toolbox, PeTTSy (Perturbation Theory Toolbox for Systems). PeTTSy is a package for MATLAB which implements a wide array of techniques for the perturbation theory and sensitivity analysis of large and complex ordinary differential equation (ODE) based models. PeTTSy is a comprehensive modelling framework that introduces a number of new approaches and that fully addresses analysis of oscillatory systems. It examines sensitivity analysis of the models to perturbations of parameters, where the perturbation timing, strength, length and overall shape can be controlled by the user. This can be done in a system-global setting, namely, the user can determine how many parameters to perturb, by how much and for how long. PeTTSy also offers the user the ability to explore the effect of the parameter perturbations on many different types of outputs: period, phase (timing of peak) and model solutions. PeTTSy can be employed on a wide range of mathematical models including free-running and forced oscillators and signalling systems. To enable experimental optimisation using the Fisher Information Matrix it efficiently allows one to combine multiple variants of a model (i.e. a model with multiple experimental conditions) in order to determine the value of new experiments. It is especially useful in the analysis of large and complex models involving many variables and parameters. PeTTSy is a comprehensive tool for analysing large and complex models of regulatory and signalling systems. It allows for simulation and analysis of models under a variety of environmental conditions and for experimental optimisation of complex combined experiments. With its unique set of tools it makes a valuable addition to the current library of sensitivity analysis toolboxes. We believe that this software will be of great use to the wider biological, systems biology and modelling communities.

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

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Enhanced bioavailability of polyaromatic hydrocarbons in the form of mucin complexes.

PubMed

Drug, Eyal; Landesman-Milo, Dalit; Belgorodsky, Bogdan; Ermakov, Natalia; Frenkel-Pinter, Moran; Fadeev, Ludmila; Peer, Dan; Gozin, Michael

2011-03-21

Increasing exposure of biological systems to large amounts of polycyclic aromatic hydrocarbons is of great public concern. Organisms have an array of biological defense mechanisms, and it is believed that mucosal gel (which covers the respiratory system, the gastrointestinal tract, etc.) provides an effective chemical shield against a range of toxic materials. However, in this work, we demonstrate, for the first time, that, upon complexation of polyaromatic hydrocarbons with mucins, enhanced bioavailability and, therefore, toxicity are obtained. This work was aimed to demonstrate how complexation of various highly hydrophobic polycyclic aromatic hydrocarbons with representative mucin glycoprotein could lead to the formation of previously undescribed materials, which exhibit increased toxicity versus pristine polycyclic aromatic hydrocarbons. In the present work, we show that a representative mucin glycoprotein, bovine submaxillary mucin, has impressive and unprecedented capabilities of binding and solubilizing water-insoluble materials in physiological solution. The complexes formed between the mucin and a series of polycyclic aromatic hydrocarbons were comprehensively characterized, and their toxicity was evaluated by both in vivo and in vitro assays. In addition, the bioavailability and membrane-penetration capabilities were tested using an internalization assay. Our results provide, for the first time, evidence of an unknown route by which hydrophobic materials may achieve higher bioavailability, penetrating some of the biological defense systems, in the form of water-soluble complexes with mucosal proteins.

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.

Entropy for the Complexity of Physiological Signal Dynamics.

PubMed

Zhang, Xiaohua Douglas

2017-01-01

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

Just Working with the Cellular Machine: A High School Game for Teaching Molecular Biology

ERIC Educational Resources Information Center

Cardoso, Fernanda Serpa; Dumpel, Renata; Gomes da Silva, Luisa B.; Rodrigues, Carlos R.; Santos, Dilvani O.; Cabral, Lucio Mendes; Castro, Helena C.

2008-01-01

Molecular biology is a difficult comprehension subject due to its high complexity, thus requiring new teaching approaches. Herein, we developed an interdisciplinary board game involving the human immune system response against a bacterial infection for teaching molecular biology at high school. Initially, we created a database with several…

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.

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

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.

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.

Using synthetic biology to make cells tomorrow's test tubes.

PubMed

Garcia, Hernan G; Brewster, Robert C; Phillips, Rob

2016-04-18

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

Supraglacial sulfur springs and associated biological activity in the Canadian high arctic - signs of life beneath the ice

USGS Publications Warehouse

Grasby, Stephen E.; Allen, Carlton C.; Longazo, Teresa G.; Lisle, John T.; Griffin, Dale W.; Beauchamp, Benoit

2003-01-01

Unique springs, discharging from the surface of an arctic glacier, release H2S and deposit native sulfur, gypsum, and calcite. The presence of sulfur in three oxidation states indicates a complex series of redox reactions. Physical and chemical conditions of the spring water and surrounding environment, as well as mineralogical and isotopic signatures, suggest biologically mediated reactions. Cell counts and DNA analyses confirm bacteria are present in the spring system, and a limited number of sequenced isolates suggests that complex communities of bacteria live within the glacial system.

Influence of using challenging tasks in biology classrooms on students' cognitive knowledge structure: an empirical video study

NASA Astrophysics Data System (ADS)

Nawani, Jigna; Rixius, Julia; Neuhaus, Birgit J.

2016-08-01

Empirical analysis of secondary biology classrooms revealed that, on average, 68% of teaching time in Germany revolved around processing tasks. Quality of instruction can thus be assessed by analyzing the quality of tasks used in classroom discourse. This quasi-experimental study analyzed how teachers used tasks in 38 videotaped biology lessons pertaining to the topic 'blood and circulatory system'. Two fundamental characteristics used to analyze tasks include: (1) required cognitive level of processing (e.g. low level information processing: repetiition, summary, define, classify and high level information processing: interpret-analyze data, formulate hypothesis, etc.) and (2) complexity of task content (e.g. if tasks require use of factual, linking or concept level content). Additionally, students' cognitive knowledge structure about the topic 'blood and circulatory system' was measured using student-drawn concept maps (N = 970 students). Finally, linear multilevel models were created with high-level cognitive processing tasks and higher content complexity tasks as class-level predictors and students' prior knowledge, students' interest in biology, and students' interest in biology activities as control covariates. Results showed a positive influence of high-level cognitive processing tasks (β = 0.07; p < .01) on students' cognitive knowledge structure. However, there was no observed effect of higher content complexity tasks on students' cognitive knowledge structure. Presented findings encourage the use of high-level cognitive processing tasks in biology instruction.

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.

Biological system interactions.

PubMed Central

Adomian, G; Adomian, G E; Bellman, R E

1984-01-01

Mathematical modeling of cellular population growth, interconnected subsystems of the body, blood flow, and numerous other complex biological systems problems involves nonlinearities and generally randomness as well. Such problems have been dealt with by mathematical methods often changing the actual model to make it tractable. The method presented in this paper (and referenced works) allows much more physically realistic solutions. PMID:6585837

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

ERIC Educational Resources Information Center

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

2006-01-01

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

The fusion of biology, computer science, and engineering: towards efficient and successful synthetic biology.

PubMed

Linshiz, Gregory; Goldberg, Alex; Konry, Tania; Hillson, Nathan J

2012-01-01

Synthetic biology is a nascent field that emerged in earnest only around the turn of the millennium. It aims to engineer new biological systems and impart new biological functionality, often through genetic modifications. The design and construction of new biological systems is a complex, multistep process, requiring multidisciplinary collaborative efforts from "fusion" scientists who have formal training in computer science or engineering, as well as hands-on biological expertise. The public has high expectations for synthetic biology and eagerly anticipates the development of solutions to the major challenges facing humanity. This article discusses laboratory practices and the conduct of research in synthetic biology. It argues that the fusion science approach, which integrates biology with computer science and engineering best practices, including standardization, process optimization, computer-aided design and laboratory automation, miniaturization, and systematic management, will increase the predictability and reproducibility of experiments and lead to breakthroughs in the construction of new biological systems. The article also discusses several successful fusion projects, including the development of software tools for DNA construction design automation, recursive DNA construction, and the development of integrated microfluidics systems.

Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology through Constructing and Testing Computational Theories--An Embodied Modeling Approach

ERIC Educational Resources Information Center

Wilensky, Uri; Reisman, Kenneth

2006-01-01

Biological phenomena can be investigated at multiple levels, from the molecular to the cellular to the organismic to the ecological. In typical biology instruction, these levels have been segregated. Yet, it is by examining the connections between such levels that many phenomena in biology, and complex systems in general, are best explained. We…

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Biomimicry Promotes the Efficiency of a 10-Step Sequential Enzymatic Reaction on Nanoparticles, Converting Glucose to Lactate.

PubMed

Mukai, Chinatsu; Gao, Lizeng; Nelson, Jacquelyn L; Lata, James P; Cohen, Roy; Wu, Lauren; Hinchman, Meleana M; Bergkvist, Magnus; Sherwood, Robert W; Zhang, Sheng; Travis, Alexander J

2017-01-02

For nanobiotechnology to achieve its potential, complex organic-inorganic systems must grow to utilize the sequential functions of multiple biological components. Critical challenges exist: immobilizing enzymes can block substrate-binding sites or prohibit conformational changes, substrate composition can interfere with activity, and multistep reactions risk diffusion of intermediates. As a result, the most complex tethered reaction reported involves only 3 enzymes. Inspired by the oriented immobilization of glycolytic enzymes on the fibrous sheath of mammalian sperm, here we show a complex reaction of 10 enzymes tethered to nanoparticles. Although individual enzyme efficiency was higher in solution, the efficacy of the 10-step pathway measured by conversion of glucose to lactate was significantly higher when tethered. To our knowledge, this is the most complex organic-inorganic system described, and it shows that tethered, multi-step biological pathways can be reconstituted in hybrid systems to carry out functions such as energy production or delivery of molecular cargo. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biomimicry promotes the efficiency of a 10-step sequential enzymatic reaction on nanoparticles, converting glucose to lactate

PubMed Central

Mukai, Chinatsu; Gao, Lizeng; Nelson, Jacquelyn L.; Lata, James P.; Cohen, Roy; Wu, Lauren; Hinchman, Meleana M.; Bergkvist, Magnus; Sherwood, Robert W.; Zhang, Sheng; Travis, Alexander J.

2016-01-01

For nanobiotechnology to achieve its potential, complex organic-inorganic systems must grow to utilize the sequential functions of multiple biological components. Critical challenges exist: immobilizing enzymes can block substrate-binding sites or prohibit conformational changes, substrate composition can interfere with activity, and multistep reactions risk diffusion of intermediates. As a result, the most complex tethered reaction reported involves only 3 enzymes. Inspired by the oriented immobilization of glycolytic enzymes on the fibrous sheath of mammalian sperm, here we show a complex reaction of 10 enzymes tethered to nanoparticles. Although individual enzyme efficiency was higher in solution, the efficacy of the 10-step pathway measured by conversion of glucose to lactate was significantly higher when tethered. To our knowledge, this is the most complex organic-inorganic system described, and it shows that tethered, multi-step biological pathways can be reconstituted in hybrid systems to carry out functions such as energy production or delivery of molecular cargo. PMID:27901298

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology.

PubMed

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-03-22

A protein complex consists of two or more proteins that are linked together through protein-protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples.

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

PubMed Central

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-01-01

A protein complex consists of two or more proteins that are linked together through protein–protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples. PMID:27011181

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.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

PubMed

Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

A survey of fuzzy logic monitoring and control utilisation in medicine.

PubMed

Mahfouf, M; Abbod, M F; Linkens, D A

2001-01-01

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for such intelligent systems driven solutions is that biological systems are so complex that the development of computerised systems within such environments is not always a straightforward exercise. In practice, a precise model may not exist for biological systems or it may be too difficult to model. In most cases fuzzy logic is considered to be an ideal tool as human minds work from approximate data, extract meaningful information and produce crisp solutions. This paper surveys the utilisation of fuzzy logic control and monitoring in medical sciences with an analysis of its possible future penetration.

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

PubMed

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems

PubMed Central

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information. PMID:23346354

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

PubMed

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

2014-10-06

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

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

PubMed Central

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

2014-01-01

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

‘Integrative Physiology 2.0’: integration of systems biology into physiology and its application to cardiovascular homeostasis

PubMed Central

Kuster, Diederik W D; Merkus, Daphne; van der Velden, Jolanda; Verhoeven, Adrie J M; Duncker, Dirk J

2011-01-01

Since the completion of the Human Genome Project and the advent of the large scaled unbiased ‘-omics’ techniques, the field of systems biology has emerged. Systems biology aims to move away from the traditional reductionist molecular approach, which focused on understanding the role of single genes or proteins, towards a more holistic approach by studying networks and interactions between individual components of networks. From a conceptual standpoint, systems biology elicits a ‘back to the future’ experience for any integrative physiologist. However, many of the new techniques and modalities employed by systems biologists yield tremendous potential for integrative physiologists to expand their tool arsenal to (quantitatively) study complex biological processes, such as cardiac remodelling and heart failure, in a truly holistic fashion. We therefore advocate that systems biology should not become/stay a separate discipline with ‘-omics’ as its playing field, but should be integrated into physiology to create ‘Integrative Physiology 2.0’. PMID:21224228

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

SABRE: a bio-inspired fault-tolerant electronic architecture.

PubMed

Bremner, P; Liu, Y; Samie, M; Dragffy, G; Pipe, A G; Tempesti, G; Timmis, J; Tyrrell, A M

2013-03-01

As electronic devices become increasingly complex, ensuring their reliable, fault-free operation is becoming correspondingly more challenging. It can be observed that, in spite of their complexity, biological systems are highly reliable and fault tolerant. Hence, we are motivated to take inspiration for biological systems in the design of electronic ones. In SABRE (self-healing cellular architectures for biologically inspired highly reliable electronic systems), we have designed a bio-inspired fault-tolerant hierarchical architecture for this purpose. As in biology, the foundation for the whole system is cellular in nature, with each cell able to detect faults in its operation and trigger intra-cellular or extra-cellular repair as required. At the next level in the hierarchy, arrays of cells are configured and controlled as function units in a transport triggered architecture (TTA), which is able to perform partial-dynamic reconfiguration to rectify problems that cannot be solved at the cellular level. Each TTA is, in turn, part of a larger multi-processor system which employs coarser grain reconfiguration to tolerate faults that cause a processor to fail. In this paper, we describe the details of operation of each layer of the SABRE hierarchy, and how these layers interact to provide a high systemic level of fault tolerance.

Speech as a breakthrough signaling resource in the cognitive evolution of biological complex adaptive systems.

PubMed

Mattei, Tobias A

2014-12-01

In self-adapting dynamical systems, a significant improvement in the signaling flow among agents constitutes one of the most powerful triggering events for the emergence of new complex behaviors. Ackermann and colleagues' comprehensive phylogenetic analysis of the brain structures involved in acoustic communication provides further evidence of the essential role which speech, as a breakthrough signaling resource, has played in the evolutionary development of human cognition viewed from the standpoint of complex adaptive system analysis.

Integrating systems biology models and biomedical ontologies

PubMed Central

2011-01-01

Background Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. PMID:21835028

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

PubMed

Guo, Wei-Feng; Zhang, Shao-Wu; Shi, Qian-Qian; Zhang, Cheng-Ming; Zeng, Tao; Chen, Luonan

2018-01-19

The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.

How Can We Improve Problem Solving in Undergraduate Biology? Applying Lessons from 30 Years of Physics Education Research

PubMed Central

Hoskinson, A.-M.; Caballero, M. D.; Knight, J. K.

2013-01-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research. PMID:23737623

Multiple Replica Repulsion Technique for Efficient Conformational Sampling of Biological Systems

PubMed Central

Malevanets, Anatoly; Wodak, Shoshana J.

2011-01-01

Here, we propose a technique for sampling complex molecular systems with many degrees of freedom. The technique, termed “multiple replica repulsion” (MRR), does not suffer from poor scaling with the number of degrees of freedom associated with common replica exchange procedures and does not require sampling at high temperatures. The algorithm involves creation of multiple copies (replicas) of the system, which interact with one another through a repulsive potential that can be applied to the system as a whole or to portions of it. The proposed scheme prevents oversampling of the most populated states and provides accurate descriptions of conformational perturbations typically associated with sampling ground-state energy wells. The performance of MRR is illustrated for three systems of increasing complexity. A two-dimensional toy potential surface is used to probe the sampling efficiency as a function of key parameters of the procedure. MRR simulations of the Met-enkephalin pentapeptide, and the 76-residue protein ubiquitin, performed in presence of explicit water molecules and totaling 32 ns each, investigate the ability of MRR to characterize the conformational landscape of the peptide, and the protein native basin, respectively. Results obtained for the enkephalin peptide reflect more closely the extensive conformational flexibility of this peptide than previously reported simulations. Those obtained for ubiquitin show that conformational ensembles sampled by MRR largely encompass structural fluctuations relevant to biological recognition, which occur on the microsecond timescale, or are observed in crystal structures of ubiquitin complexes with other proteins. MRR thus emerges as a very promising simple and versatile technique for modeling the structural plasticity of complex biological systems. PMID:21843487

The "Biologically-Inspired Computing" Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2007-01-01

Self-managing systems, whether viewed from the perspective of Autonomic Computing, or from that of another initiative, offers a holistic vision for the development and evolution of biologically-inspired computer-based systems. It aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing costs. A case can certainly be made that all computer-based systems should exhibit autonomic properties [6], and we envisage greater interest in, and uptake of, autonomic principles in future system development.

Synthetic mixed-signal computation in living cells

PubMed Central

Rubens, Jacob R.; Selvaggio, Gianluca; Lu, Timothy K.

2016-01-01

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells. PMID:27255669

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

Yu, Ai-Qun; Pratomo Juwono, Nina Kurniasih; Synthetic Biology Research Program, National University of Singapore, Singapore

Fatty acid derivatives, such as hydroxy fatty acids, fatty alcohols, fatty acid methyl/ethyl esters, and fatty alka(e)nes, have a wide range of industrial applications including plastics, lubricants, and fuels. Currently, these chemicals are obtained mainly through chemical synthesis, which is complex and costly, and their availability from natural biological sources is extremely limited. Metabolic engineering of microorganisms has provided a platform for effective production of these valuable biochemicals. Notably, synthetic biology-based metabolic engineering strategies have been extensively applied to refactor microorganisms for improved biochemical production. Here, we reviewed: (i) the current status of metabolic engineering of microbes that produce fattymore » acid-derived valuable chemicals, and (ii) the recent progress of synthetic biology approaches that assist metabolic engineering, such as mRNA secondary structure engineering, sensor-regulator system, regulatable expression system, ultrasensitive input/output control system, and computer science-based design of complex gene circuits. Furthermore, key challenges and strategies were discussed. Finally, we concluded that synthetic biology provides useful metabolic engineering strategies for economically viable production of fatty acid-derived valuable chemicals in engineered microbes.« less

Production of Fatty Acid-Derived Valuable Chemicals in Synthetic Microbes

PubMed Central

Yu, Ai-Qun; Pratomo Juwono, Nina Kurniasih; Leong, Susanna Su Jan; Chang, Matthew Wook

2014-01-01

Fatty acid derivatives, such as hydroxy fatty acids, fatty alcohols, fatty acid methyl/ethyl esters, and fatty alka(e)nes, have a wide range of industrial applications including plastics, lubricants, and fuels. Currently, these chemicals are obtained mainly through chemical synthesis, which is complex and costly, and their availability from natural biological sources is extremely limited. Metabolic engineering of microorganisms has provided a platform for effective production of these valuable biochemicals. Notably, synthetic biology-based metabolic engineering strategies have been extensively applied to refactor microorganisms for improved biochemical production. Here, we reviewed: (i) the current status of metabolic engineering of microbes that produce fatty acid-derived valuable chemicals, and (ii) the recent progress of synthetic biology approaches that assist metabolic engineering, such as mRNA secondary structure engineering, sensor-regulator system, regulatable expression system, ultrasensitive input/output control system, and computer science-based design of complex gene circuits. Furthermore, key challenges and strategies were discussed. Finally, we concluded that synthetic biology provides useful metabolic engineering strategies for economically viable production of fatty acid-derived valuable chemicals in engineered microbes. PMID:25566540

A mathematical applications into the cells.

PubMed

Tiwari, Manjul

2012-01-01

Biology has become the new "physics" of mathematics, one of the areas of greatest mathematical applications. In turn, mathematics has provided powerful tools and metaphors to approach the astonishing complexity of biological systems. This has allowed the development of sound theoretical frameworks. Here, in this review article, some of the most significant contributions of mathematics to biology, ranging from population genetics, to developmental biology, and to networks of species interactions are summarized.

Light microscopy applications in systems biology: opportunities and challenges

PubMed Central

2013-01-01

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology. PMID:23578051

An integrative approach to inferring biologically meaningful gene modules

PubMed Central

2011-01-01

Background The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. Results We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. Conclusions The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level. PMID:21791051

Short-term bioassay of complex organic mixtures. Part II. Mutagenicity testing

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

Epler, J.L.; Clark, B.R.; Ho, C.

1978-01-01

The feasibility of using short-term mutagenicity assays to predict the potential biohazard of various crude and complex test materials has been examined in a coupled chemical and biological approach. The principal focus of the research has involved the preliminary chemical characterizatiion and preparation for bioassay, followed by testing in the Salmonella histidine reversion assay system. The mutagenicity tests are intended to act as predictors of profound long-range health effects such as mutagenesis and/or carcinogenesis; act as a mechanism to rapidly isolate and identify a hazardous agent in a complex mixture; and function as a measure of biological activity correlating baselinemore » data with changes in process conditions. Since complex mixtures can be fractionated and approached in these short-term assays, information reflecting on the actual compounds responsible for the biological effect may be accumulated.« less

Behavior of the gypsy moth life system model and development of synoptic model formulations

Treesearch

J. J. Colbert; Xu Rumei

1991-01-01

Aims of the research: The gypsy moth life system model (GMLSM) is a complex model which incorporates numerous components (both biotic and abiotic) and ecological processes. It is a detailed simulation model which has much biological reality. However, it has not yet been tested with life system data. For such complex models, evaluation and testing cannot be adequately...

Systems Biology Approaches to the Study of Biological Networks Underlying Alzheimer's Disease: Role of miRNAs.

PubMed

Roth, Wera; Hecker, David; Fava, Eugenio

2016-01-01

MicroRNAs (miRNAs) are emerging as significant regulators of mRNA complexity in the human central nervous system (CNS) thereby controlling distinct gene expression profiles in a spatio-temporal manner during development, neuronal plasticity, aging and (age-related) neurodegeneration, including Alzheimer's disease (AD). Increasing effort is expended towards dissecting and deciphering the molecular and genetic mechanisms of neurobiological and pathological functions of these brain-enriched miRNAs. Along these lines, recent data pinpoint distinct miRNAs and miRNA networks being linked to APP splicing, processing and Aβ pathology (Lukiw et al., Front Genet 3:327, 2013), and furthermore, to the regulation of tau and its cellular subnetworks (Lau et al., EMBO Mol Med 5:1613, 2013), altogether underlying the onset and propagation of Alzheimer's disease. MicroRNA profiling studies in Alzheimer's disease suffer from poor consensus which is an acknowledged concern in the field, and constitutes one of the current technical challenges. Hence, a strong demand for experimental and computational systems biology approaches arises, to incorporate and integrate distinct levels of information and scientific knowledge into a complex system of miRNA networks in the context of the transcriptome, proteome and metabolome in a given cellular environment. Here, we will discuss the state-of-the-art technologies and computational approaches on hand that may lead to a deeper understanding of the complex biological networks underlying the pathogenesis of Alzheimer's disease.

cellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology

PubMed Central

Johnson, Graham T.; Autin, Ludovic; Al-Alusi, Mostafa; Goodsell, David S.; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10−7–10−8m) between molecular and cellular biology. cellPACK’s modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive 3D models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is currently available as open source code, with tools for validation of models and with recipes and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators, and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org. PMID:25437435

Animal models and conserved processes

PubMed Central

2012-01-01

Background The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation? Methods We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes. Results Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes. Conclusion We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible. PMID:22963674

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

NASA Astrophysics Data System (ADS)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Introduction to symposium: Arthropods and wildlife conservation: synergy in complex biological systems

USDA-ARS?s Scientific Manuscript database

The symposium will discuss the effects of arthropods and other stressors on wildlife conservation programs. Speakers with affiliations in wildlife biology, parasitology and entomology will be included in the program. Research of national and international interest will be presented....

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.

Quantitative Interpretation of Multifrequency Multimode EPR Spectra of Metal Containing Proteins, Enzymes, and Biomimetic Complexes.

PubMed

Petasis, Doros T; Hendrich, Michael P

2015-01-01

Electron paramagnetic resonance (EPR) spectroscopy has long been a primary method for characterization of paramagnetic centers in materials and biological complexes. Transition metals in biological complexes have valence d-orbitals that largely define the chemistry of the metal centers. EPR spectra are distinctive for metal type, oxidation state, protein environment, substrates, and inhibitors. The study of many metal centers in proteins, enzymes, and biomimetic complexes has led to the development of a systematic methodology for quantitative interpretation of EPR spectra from a wide array of metal containing complexes. The methodology is now contained in the computer program SpinCount. SpinCount allows simulation of EPR spectra from any sample containing multiple species composed of one or two metals in any spin state. The simulations are quantitative, thus allowing determination of all species concentrations in a sample directly from spectra. This chapter will focus on applications to transition metals in biological systems using EPR spectra from multiple microwave frequencies and modes. © 2015 Elsevier Inc. All rights reserved.

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

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

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

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

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-01-01

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

A 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

Agent-based modelling in synthetic biology.

PubMed

Gorochowski, Thomas E

2016-11-30

Biological systems exhibit complex behaviours that emerge at many different levels of organization. These span the regulation of gene expression within single cells to the use of quorum sensing to co-ordinate the action of entire bacterial colonies. Synthetic biology aims to make the engineering of biology easier, offering an opportunity to control natural systems and develop new synthetic systems with useful prescribed behaviours. However, in many cases, it is not understood how individual cells should be programmed to ensure the emergence of a required collective behaviour. Agent-based modelling aims to tackle this problem, offering a framework in which to simulate such systems and explore cellular design rules. In this article, I review the use of agent-based models in synthetic biology, outline the available computational tools, and provide details on recently engineered biological systems that are amenable to this approach. I further highlight the challenges facing this methodology and some of the potential future directions. © 2016 The Author(s).

Complex Homology and the Evolution of Nervous Systems

PubMed Central

Liebeskind, Benjamin J.; Hillis, David M.; Zakon, Harold H.; Hofmann, Hans A.

2016-01-01

We examine the complex evolution of animal nervous systems and discuss the ramifications of this complexity for inferring the nature of early animals. Although reconstructing the origins of nervous systems remains a central challenge in biology, and the phenotypic complexity of early animals remains controversial, a compelling picture is emerging. We now know that the nervous system and other key animal innovations contain a large degree of homoplasy, at least on the molecular level. Conflicting hypotheses about early nervous system evolution are due primarily to differences in the interpretation of this homoplasy. We highlight the need for explicit discussion of assumptions and discuss the limitations of current approaches for inferring ancient phenotypic states. PMID:26746806

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.…

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...

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

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.

Anion-exchange high-performance liquid chromatography of water-soluble chromium (VI) and chromium (III) complexes in biological materials.

PubMed

Suzuki, Y

1987-04-10

A high-performance anion-exchange liquid chromatograph coupled to visible-range (370 nm) and UV (280 nm) detectors and an atomic-absorption spectrometer allowed the rapid determination of CrVI and/or complexes of CrIII in rat plasma, erythrocyte lysate and liver supernatant treated with CrVI or CrIII in vitro. CrVI in the eluates was determined using both the visible-range detector and atomic-absorption spectrometer (AAS). The detection limits of CrVI in standard solutions using these methods were 2 and 5 ng (signal-to-noise ratio = 2), respectively. Separations of the biological components and of CrIII complexes were monitored by UV and AAS analyses, respectively. Time-related decreases of CrVI accompanied by increases in CrIII complexes were observed, indicating the reduction of CrVI by some of the biological components. The reduction rates were considerably higher in the liver supernatant and erythrocyte lysate than in the plasma. These results indicate that the anion-exchange high-performance liquid chromatographic system is useful for simultaneous determination of CrVI and CrIII complexes in biological materials.

The Network Organization of Cancer-associated Protein Complexes in Human Tissues

PubMed Central

Zhao, Jing; Lee, Sang Hoon; Huss, Mikael; Holme, Petter

2013-01-01

Differential gene expression profiles for detecting disease genes have been studied intensively in systems biology. However, it is known that various biological functions achieved by proteins follow from the ability of the protein to form complexes by physically binding to each other. In other words, the functional units are often protein complexes rather than individual proteins. Thus, we seek to replace the perspective of disease-related genes by disease-related complexes, exemplifying with data on 39 human solid tissue cancers and their original normal tissues. To obtain the differential abundance levels of protein complexes, we apply an optimization algorithm to genome-wide differential expression data. From the differential abundance of complexes, we extract tissue- and cancer-selective complexes, and investigate their relevance to cancer. The method is supported by a clustering tendency of bipartite cancer-complex relationships, as well as a more concrete and realistic approach to disease-related proteomics. PMID:23567845

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

Microfluidic 3D cell culture: potential application for tissue-based bioassays

PubMed Central

Li, XiuJun (James); Valadez, Alejandra V.; Zuo, Peng; Nie, Zhihong

2014-01-01

Current fundamental investigations of human biology and the development of therapeutic drugs, commonly rely on two-dimensional (2D) monolayer cell culture systems. However, 2D cell culture systems do not accurately recapitulate the structure, function, physiology of living tissues, as well as highly complex and dynamic three-dimensional (3D) environments in vivo. The microfluidic technology can provide micro-scale complex structures and well-controlled parameters to mimic the in vivo environment of cells. The combination of microfluidic technology with 3D cell culture offers great potential for in vivo-like tissue-based applications, such as the emerging organ-on-a-chip system. This article will review recent advances in microfluidic technology for 3D cell culture and their biological applications. PMID:22793034

Genetic control of root growth: from genes to networks

PubMed Central

Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B.; Ristova, Daniela; Busch, Wolfgang

2016-01-01

Background Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. Scope This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. Conclusions While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. PMID:26558398

The cosmological model of eternal inflation and the transition from chance to biological evolution in the history of life

PubMed Central

Koonin, Eugene V

2007-01-01

Background Recent developments in cosmology radically change the conception of the universe as well as the very notions of "probable" and "possible". The model of eternal inflation implies that all macroscopic histories permitted by laws of physics are repeated an infinite number of times in the infinite multiverse. In contrast to the traditional cosmological models of a single, finite universe, this worldview provides for the origin of an infinite number of complex systems by chance, even as the probability of complexity emerging in any given region of the multiverse is extremely low. This change in perspective has profound implications for the history of any phenomenon, and life on earth cannot be an exception. Hypothesis Origin of life is a chicken and egg problem: for biological evolution that is governed, primarily, by natural selection, to take off, efficient systems for replication and translation are required, but even barebones cores of these systems appear to be products of extensive selection. The currently favored (partial) solution is an RNA world without proteins in which replication is catalyzed by ribozymes and which serves as the cradle for the translation system. However, the RNA world faces its own hard problems as ribozyme-catalyzed RNA replication remains a hypothesis and the selective pressures behind the origin of translation remain mysterious. Eternal inflation offers a viable alternative that is untenable in a finite universe, i.e., that a coupled system of translation and replication emerged by chance, and became the breakthrough stage from which biological evolution, centered around Darwinian selection, took off. A corollary of this hypothesis is that an RNA world, as a diverse population of replicating RNA molecules, might have never existed. In this model, the stage for Darwinian selection is set by anthropic selection of complex systems that rarely but inevitably emerge by chance in the infinite universe (multiverse). Conclusion The plausibility of different models for the origin of life on earth directly depends on the adopted cosmological scenario. In an infinite universe (multiverse), emergence of highly complex systems by chance is inevitable. Therefore, under this cosmology, an entity as complex as a coupled translation-replication system should be considered a viable breakthrough stage for the onset of biological evolution. Reviewers This article was reviewed by Eric Bapteste, David Krakauer, Sergei Maslov, and Itai Yanai. PMID:17540027

The cosmological model of eternal inflation and the transition from chance to biological evolution in the history of life.

PubMed

Koonin, Eugene V

2007-05-31

Recent developments in cosmology radically change the conception of the universe as well as the very notions of "probable" and "possible". The model of eternal inflation implies that all macroscopic histories permitted by laws of physics are repeated an infinite number of times in the infinite multiverse. In contrast to the traditional cosmological models of a single, finite universe, this worldview provides for the origin of an infinite number of complex systems by chance, even as the probability of complexity emerging in any given region of the multiverse is extremely low. This change in perspective has profound implications for the history of any phenomenon, and life on earth cannot be an exception. Origin of life is a chicken and egg problem: for biological evolution that is governed, primarily, by natural selection, to take off, efficient systems for replication and translation are required, but even barebones cores of these systems appear to be products of extensive selection. The currently favored (partial) solution is an RNA world without proteins in which replication is catalyzed by ribozymes and which serves as the cradle for the translation system. However, the RNA world faces its own hard problems as ribozyme-catalyzed RNA replication remains a hypothesis and the selective pressures behind the origin of translation remain mysterious. Eternal inflation offers a viable alternative that is untenable in a finite universe, i.e., that a coupled system of translation and replication emerged by chance, and became the breakthrough stage from which biological evolution, centered around Darwinian selection, took off. A corollary of this hypothesis is that an RNA world, as a diverse population of replicating RNA molecules, might have never existed. In this model, the stage for Darwinian selection is set by anthropic selection of complex systems that rarely but inevitably emerge by chance in the infinite universe (multiverse). The plausibility of different models for the origin of life on earth directly depends on the adopted cosmological scenario. In an infinite universe (multiverse), emergence of highly complex systems by chance is inevitable. Therefore, under this cosmology, an entity as complex as a coupled translation-replication system should be considered a viable breakthrough stage for the onset of biological evolution. This article was reviewed by Eric Bapteste, David Krakauer, Sergei Maslov, and Itai Yanai.

Luminescent Rhenium(I) and Iridium(III) Polypyridine Complexes as Biological Probes, Imaging Reagents, and Photocytotoxic Agents.

PubMed

Lo, Kenneth Kam-Wing

2015-12-15

Although the interactions of transition metal complexes with biological molecules have been extensively studied, the use of luminescent transition metal complexes as intracellular sensors and bioimaging reagents has not been a focus of research until recently. The main advantages of luminescent transition metal complexes are their high photostability, long-lived phosphorescence that allows time-resolved detection, and large Stokes shifts that can minimize the possible self-quenching effect. Also, by the use of transition metal complexes, the degree of cellular uptake can be readily determined using inductively coupled plasma mass spectrometry. For more than a decade, we have been interested in the development of luminescent transition metal complexes as covalent labels and noncovalent probes for biological molecules. We argue that many transition metal polypyridine complexes display triplet charge transfer ((3)CT) emission that is highly sensitive to the local environment of the complexes. Hence, the biological labeling and binding interactions can be readily reflected by changes in the photophysical properties of the complexes. In this laboratory, we have modified luminescent tricarbonylrhenium(I) and bis-cyclometalated iridium(III) polypyridine complexes of general formula [Re(bpy-R(1))(CO)3(py-R(2))](+) and [Ir(ppy-R(3))2(bpy-R(4))](+), respectively, with reactive functional groups and used them to label the amine and sulfhydryl groups of biomolecules such as oligonucleotides, amino acids, peptides, and proteins. Additionally, using a range of biological substrates such as biotin, estradiol, and indole, we have designed luminescent rhenium(I) and iridium(III) polypyridine complexes as noncovalent probes for biological receptors. The interesting results generated from these studies have prompted us to investigate the possible applications of luminescent transition metal complexes in intracellular systems. Thus, in the past few years, we have developed an interest in the cytotoxic activity, cellular uptake, and bioimaging applications of these complexes. Additionally, we and other research groups have demonstrated that many transition metal complexes have facile cellular uptake and organelle-localization properties and that their cytotoxic activity can be readily controlled. For example, complexes that can target the nucleus, nucleolus, mitochondria, lysosomes, endoplasmic reticulum, and Golgi apparatus have been identified. We anticipate that this selective localization property can be utilized in the development of intracellular sensors and bioimaging reagents. Thus, we have functionalized luminescent rhenium(I) and iridium(III) polypyridine complexes with various pendants, including molecule-binding moieties, sugar molecules, bioorthogonal functional groups, and polymeric chains such as poly(ethylene glycol) and polyethylenimine, and examined their potentials as biological reagents. This Account describes our design of luminescent rhenium(I) and iridium(III) polypyridine complexes and explains how they can serve as a new generation of biological reagents for diagnostic and therapeutic applications.

An inexpensive, temporally-integrated system for monitoring occurrence and biological effects of aquatic contaminants in the field

EPA Science Inventory

Assessment of potential ecological risks of complex contaminant mixtures in the environment requires integrated chemical and biological approaches. Instrumental analysis of environmental samples alone can identify contaminants, but provides only limited insights as to possible a...

An inexpensive, temporally-integrated system for monitoring occurrence and biological effects of contaminants in the field

EPA Science Inventory

Assessing potential biological impacts of complex mixtures of contaminants in aquatic environments is an ongoing challenge for ecotoxicologists. Instrumental analysis of site waters alone can identify contaminants but provides only limited insights as to possible adverse effects...

Back to the biology in systems biology: what can we learn from biomolecular networks?

PubMed

Huang, Sui

2004-02-01

Genome-scale molecular networks, including protein interaction and gene regulatory networks, have taken centre stage in the investigation of the burgeoning disciplines of systems biology and biocomplexity. What do networks tell us? Some see in networks simply the comprehensive, detailed description of all cellular pathways, others seek in networks simple, higher-order qualities that emerge from the collective action of the individual pathways. This paper discusses networks from an encompassing category of thinking that will hopefully help readers to bridge the gap between these polarised viewpoints. Systems biology so far has emphasised the characterisation of large pathway maps. Now one has to ask: where is the actual biology in 'systems biology'? As structures midway between genome and phenome, and by serving as an 'extended genotype' or an 'elementary phenotype', molecular networks open a new window to the study of evolution and gene function in complex living systems. For the study of evolution, features in network topology offer a novel starting point for addressing the old debate on the relative contributions of natural selection versus intrinsic constraints to a particular trait. To study the function of genes, it is necessary not only to see them in the context of gene networks, but also to reach beyond describing network topology and to embrace the global dynamics of networks that will reveal higher-order, collective behaviour of the interacting genes. This will pave the way to understanding how the complexity of genome-wide molecular networks collapses to produce a robust whole-cell behaviour that manifests as tightly-regulated switching between distinct cell fates - the basis for multicellular life.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

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....

Analysis and Design of Complex Network Environments

DTIC Science & Technology

2012-03-01

and J. Lowe, “The myths and facts behind cyber security risks for industrial control systems ,” in the Proceedings of the VDE Kongress, VDE Congress...questions about 1) how to model them, 2) the design of experiments necessary to discover their structure (and thus adapt system inputs to optimize the...theoretical work that clarifies fundamental limitations of complex networks with network engineering and systems biology to implement specific designs and

Systems biology solutions for biochemical production challenges.

PubMed

Hansen, Anne Sofie Lærke; Lennen, Rebecca M; Sonnenschein, Nikolaus; Herrgård, Markus J

2017-06-01

There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A System-Level Pathway-Phenotype Association Analysis Using Synthetic Feature Random Forest

PubMed Central

Pan, Qinxin; Hu, Ting; Malley, James D.; Andrew, Angeline S.; Karagas, Margaret R.; Moore, Jason H.

2015-01-01

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations. PMID:24535726

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

PubMed

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

2018-04-01

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

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

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.

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

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.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at 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 enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, 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 systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at 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 enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, 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 systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena.

PubMed

De Domenico, Manlio

2017-04-21

Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

NASA Astrophysics Data System (ADS)

De Domenico, Manlio

2017-04-01

Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.

Genomewide effects of peroxisome proliferator-activated receptor gamma in macrophages and dendritic cells--revealing complexity through systems biology.

PubMed

Cuaranta-Monroy, Ixchelt; Kiss, Mate; Simandi, Zoltan; Nagy, Laszlo

2015-09-01

Systems biology approaches have become indispensable tools in biomedical and basic research. These data integrating bioinformatic methods gained prominence after high-throughput technologies became available to investigate complex cellular processes, such as transcriptional regulation and protein-protein interactions, on a scale that had not been studied before. Immunology is one of the medical fields that systems biology impacted profoundly due to the plasticity of cell types involved and the accessibility of a wide range of experimental models. In this review, we summarize the most important recent genomewide studies exploring the function of peroxisome proliferator-activated receptor γ in macrophages and dendritic cells. PPARγ ChIP-seq experiments were performed in adipocytes derived from embryonic stem cells to complement the existing data sets and to provide comparators to macrophage data. Finally, lists of regulated genes generated from such experiments were analysed with bioinformatics and system biology approaches. We show that genomewide studies utilizing high-throughput data acquisition methods made it possible to gain deeper insights into the role of PPARγ in these immune cell types. We also demonstrate that analysis and visualization of data using network-based approaches can be used to identify novel genes and functions regulated by the receptor. The example of PPARγ in macrophages and dendritic cells highlights the crucial importance of systems biology approaches in establishing novel cellular functions for long-known signaling pathways. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

On the search for design principles in biological systems.

PubMed

Poyatos, Juan F

2012-01-01

The search for basic concepts and underlying principles was at the core of the systems approach to science and technology. This approach was somehow abandoned in mainstream biology after its initial proposal, due to the rise and success of molecular biology. This situation has changed. The accumulated knowledge of decades of molecular studies in combination with new technological advances, while further highlighting the intricacies of natural systems, is also bringing back the quest-for-principles research program. Here, I present two lessons that I derived from my own quest: the importance of studying biological information processing to identify common principles in seemingly unrelated contexts and the adequacy of using known design principles at one level of biological organization as a valuable tool to help recognizing principles at an alternative one. These and additional lessons should contribute to the ultimate goal of establishing principles able to integrate the many scales of biological complexity.

Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3.

PubMed

Moodie, Stuart; Le Novère, Nicolas; Demir, Emek; Mi, Huaiyu; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Process Description language represents biological entities and processes between these entities within a network. SBGN PD focuses on the mechanistic description and temporal dependencies of biological interactions and transformations. The nodes (elements) are split into entity nodes describing, e.g., metabolites, proteins, genes and complexes, and process nodes describing, e.g., reactions and associations. The edges (connections) provide descriptions of relationships (or influences) between the nodes, such as consumption, production, stimulation and inhibition. Among all three languages of SBGN, PD is the closest to metabolic and regulatory pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Structural Biology of Supramolecular Assemblies by Magic Angle Spinning NMR Spectroscopy

PubMed Central

Quinn, Caitlin M.; Polenova, Tatyana

2017-01-01

In recent years, exciting developments in instrument technology and experimental methodology have advanced the field of magic angle spinning (MAS) NMR to new heights. Contemporary MAS NMR yields atomic-level insights into structure and dynamics of an astounding range of biological systems, many of which cannot be studied by other methods. With the advent of fast magic angle spinning, proton detection, and novel pulse sequences, large supramolecular assemblies, such as cytoskeletal proteins and intact viruses, are now accessible for detailed analysis. In this review, we will discuss the current MAS NMR methodologies that enable characterization of complex biomolecular systems and will present examples of applications to several classes of assemblies comprising bacterial and mammalian cytoskeleton as well as HIV-1 and bacteriophage viruses. The body of work reviewed herein is representative of the recent advancements in the field, with respect to the complexity of the systems studied, the quality of the data, and the significance to the biology. PMID:28093096

Systems Biology Approaches for Discovering Biomarkers for Traumatic Brain Injury

PubMed Central

Feala, Jacob D.; AbdulHameed, Mohamed Diwan M.; Yu, Chenggang; Dutta, Bhaskar; Yu, Xueping; Schmid, Kara; Dave, Jitendra; Tortella, Frank

2013-01-01

Abstract The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates. PMID:23510232

The role of fractional calculus in modeling biological phenomena: A review

NASA Astrophysics Data System (ADS)

Ionescu, C.; Lopes, A.; Copot, D.; Machado, J. A. T.; Bates, J. H. T.

2017-10-01

This review provides the latest developments and trends in the application of fractional calculus (FC) in biomedicine and biology. Nature has often showed to follow rather simple rules that lead to the emergence of complex phenomena as a result. Of these, the paper addresses the properties in respiratory lung tissue, whose natural solutions arise from the midst of FC in the form of non-integer differ-integral solutions and non-integer parametric models. Diffusion of substances in human body, e.g. drug diffusion, is also a phenomena well known to be captured with such mathematical models. FC has been employed in neuroscience to characterize the generation of action potentials and spiking patters but also in characterizing bio-systems (e.g. vegetable tissues). Despite the natural complexity, biological systems belong as well to this class of systems, where FC has offered parsimonious yet accurate models. This review paper is a collection of results and literature reports who are essential to any versed engineer with multidisciplinary applications and bio-medical in particular.

Systems Biology and Biomechanical Model of Heart Failure

PubMed Central

Louridas, George E; Lourida, Katerina G

2012-01-01

Heart failure is seen as a complex disease caused by a combination of a mechanical disorder, cardiac remodeling and neurohormonal activation. To define heart failure the systems biology approach integrates genes and molecules, interprets the relationship of the molecular networks with modular functional units, and explains the interaction between mechanical dysfunction and cardiac remodeling. The biomechanical model of heart failure explains satisfactorily the progression of myocardial dysfunction and the development of clinical phenotypes. The earliest mechanical changes and stresses applied in myocardial cells and/or myocardial loss or dysfunction activate left ventricular cavity remodeling and other neurohormonal regulatory mechanisms such as early release of natriuretic peptides followed by SAS and RAAS mobilization. Eventually the neurohormonal activation and the left ventricular remodeling process are leading to clinical deterioration of heart failure towards a multi-organic damage. It is hypothesized that approaching heart failure with the methodology of systems biology we promote the elucidation of its complex pathophysiology and most probably we can invent new therapeutic strategies. PMID:22935019

An inexpensive, temporally-integrated system for monitoring occurrence and biological effects of contaminants in the field (Poster)

EPA Science Inventory

Assessing potential biological impacts of complex mixtures of contaminants in aquatic environments is an ongoing challenge for ecotoxicologists. Instrumental analysis of site waters alone can identify contaminants but provides only limited insights as to possible adverse effects...

A logic-based dynamic modeling approach to explicate the evolution of the central dogma of molecular biology.

PubMed

Jafari, Mohieddin; Ansari-Pour, Naser; Azimzadeh, Sadegh; Mirzaie, Mehdi

It is nearly half a century past the age of the introduction of the Central Dogma (CD) of molecular biology. This biological axiom has been developed and currently appears to be all the more complex. In this study, we modified CD by adding further species to the CD information flow and mathematically expressed CD within a dynamic framework by using Boolean network based on its present-day and 1965 editions. We show that the enhancement of the Dogma not only now entails a higher level of complexity, but it also shows a higher level of robustness, thus far more consistent with the nature of biological systems. Using this mathematical modeling approach, we put forward a logic-based expression of our conceptual view of molecular biology. Finally, we show that such biological concepts can be converted into dynamic mathematical models using a logic-based approach and thus may be useful as a framework for improving static conceptual models in biology.

[Application of microelectronics CAD tools to synthetic biology].

PubMed

Madec, Morgan; Haiech, Jacques; Rosati, Élise; Rezgui, Abir; Gendrault, Yves; Lallement, Christophe

2017-02-01

Synthetic biology is an emerging science that aims to create new biological functions that do not exist in nature, based on the knowledge acquired in life science over the last century. Since the beginning of this century, several projects in synthetic biology have emerged. The complexity of the developed artificial bio-functions is relatively low so that empirical design methods could be used for the design process. Nevertheless, with the increasing complexity of biological circuits, this is no longer the case and a large number of computer aided design softwares have been developed in the past few years. These tools include languages for the behavioral description and the mathematical modelling of biological systems, simulators at different levels of abstraction, libraries of biological devices and circuit design automation algorithms. All of these tools already exist in other fields of engineering sciences, particularly in microelectronics. This is the approach that is put forward in this paper. © 2017 médecine/sciences – Inserm.

A logic-based dynamic modeling approach to explicate the evolution of the central dogma of molecular biology

PubMed Central

Jafari, Mohieddin; Ansari-Pour, Naser; Azimzadeh, Sadegh; Mirzaie, Mehdi

2017-01-01

It is nearly half a century past the age of the introduction of the Central Dogma (CD) of molecular biology. This biological axiom has been developed and currently appears to be all the more complex. In this study, we modified CD by adding further species to the CD information flow and mathematically expressed CD within a dynamic framework by using Boolean network based on its present-day and 1965 editions. We show that the enhancement of the Dogma not only now entails a higher level of complexity, but it also shows a higher level of robustness, thus far more consistent with the nature of biological systems. Using this mathematical modeling approach, we put forward a logic-based expression of our conceptual view of molecular biology. Finally, we show that such biological concepts can be converted into dynamic mathematical models using a logic-based approach and thus may be useful as a framework for improving static conceptual models in biology. PMID:29267315

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

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.

The Emergence of Temporal Structures in Dynamical Systems

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

2010-10-01

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

Modeling acclimatization by hybrid systems: condition changes alter biological system behavior models.

PubMed

Assar, Rodrigo; Montecino, Martín A; Maass, Alejandro; Sherman, David J

2014-07-01

In order to describe the dynamic behavior of a complex biological system, it is useful to combine models integrating processes at different levels and with temporal dependencies. Such combinations are necessary for modeling acclimatization, a phenomenon where changes in environmental conditions can induce drastic changes in the behavior of a biological system. In this article we formalize the use of hybrid systems as a tool to model this kind of biological behavior. A modeling scheme called strong switches is proposed. It allows one to take into account both minor adjustments to the coefficients of a continuous model, and, more interestingly, large-scale changes to the structure of the model. We illustrate the proposed methodology with two applications: acclimatization in wine fermentation kinetics, and acclimatization of osteo-adipo differentiation system linking stimulus signals to bone mass. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Measurement of complex permittivities of biological materials and human skin in vivo in the frequency band

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

Ghodgaonkar, D.K.

1987-01-01

A new method, namely, modified infinite sample method, has been developed which is particularly suitable for millimeter-wave dielectric measurements of biological materials. In this method, an impedance transformer is used which reduces the reflectivity of the biological sample. Because of the effect of introducing impendance transformer, the measured reflection coefficients are more sensitive to the complex permittivities of biological samples. For accurate measurement of reflection coefficients, two automated measurment systems were developed which cover the frequencies range of 26.5-60 GHz. An uncertainty analysis was performed to get an estimate of the errors in the measured complex permittivities. The dielectric propertiesmore » were measured for 10% saline solution, whole human blood, 200 mg/ml bovine serum albumin (BSA) solution and suspension of Saccharomyces cerevisiae cells. The Maxwell-Fricke equation, which is derived from dielectric mixture theory, was used for determination bound water in BSA solution. The results of all biological samples were interpreted by fitting Debye relaxation and Cole-Cole model. It is observed that the dielectric data for the biological materials can be explained on the basis of Debye relaxation of water molecule.« less

Managing bioengineering complexity with AI techniques.

PubMed

Beal, Jacob; Adler, Aaron; Yaman, Fusun

2016-10-01

Our capabilities for systematic design and engineering of biological systems are rapidly increasing. Effectively engineering such systems, however, requires the synthesis of a rapidly expanding and changing complex body of knowledge, protocols, and methodologies. Many of the problems in managing this complexity, however, appear susceptible to being addressed by artificial intelligence (AI) techniques, i.e., methods enabling computers to represent, acquire, and employ knowledge. Such methods can be employed to automate physical and informational "routine" work and thus better allow humans to focus their attention on the deeper scientific and engineering issues. This paper examines the potential impact of AI on the engineering of biological organisms through the lens of a typical organism engineering workflow. We identify a number of key opportunities for significant impact, as well as challenges that must be overcome. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Biological control of appetite: A daunting complexity.

PubMed

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

2017-03-01

This review summarizes a portion of the discussions of an NIH Workshop (Bethesda, MD, 2015) titled "Self-Regulation of Appetite-It's Complicated," which focused on the biological aspects of appetite regulation. This review summarizes the key biological inputs of appetite regulation and their implications for body weight regulation. 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. It is only beginning to be understood 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 considered is a common theme that pervaded the workshop discussions, which was individual variability. 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. © 2017 The Obesity Society.

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

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

Near-optimal experimental design for model selection in systems biology.

PubMed

Busetto, Alberto Giovanni; Hauser, Alain; Krummenacher, Gabriel; Sunnåker, Mikael; Dimopoulos, Sotiris; Ong, Cheng Soon; Stelling, Jörg; Buhmann, Joachim M

2013-10-15

Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

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.

Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise. PMID:22187523

Robust design of biological circuits: evolutionary systems biology approach.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

National Nanotechnology Initiative: Driving Innovation & Competitiveness

DTIC Science & Technology

2007-09-19

networking & IT National Nanotechnology Initiative Complex biological systems Environment Next Generation Air Transportation Systems Federal scientific ... collections Science of Science Policy Slide 9: A little history about the National Nanotechnology Initiative: The interagency program was

How do biological systems discriminate among physically similar ions?

PubMed

Diamond, J M

1975-10-01

This paper reviews the history of understanding how biological systems can discriminate so strikingly among physically similar ions, especially alkali cations. Appreciation of qualitative regularities ("permitted sequences") and quantitative regularities ("selectivity isotherms") in ion selectivity grew first from studies of ion exchangers and glass electrodes, then of biological systems such as enzymes and cell membranes, and most recently of lipid bilayers doped with model pores and carriers. Discrimination of ions depends on both electrostatic and steric forces. "Black-box" studies on intact biological membranes have in some cases yielded molecular clues to the structure of the actual biological pores and carriers. Major current problems involve the extraction of these molecules; how to do it, what to do when it is achieved, and how (and if) it is relevant to the central problems of membrane function. Further advances are expected soon from studies of rate barriers within membranes, of voltage-dependent ("excitable") conducting channels, and of increasingly complex model systems and biological membranes.

Biological roles of glycans

PubMed Central

Varki, Ajit

2017-01-01

Abstract Simple and complex carbohydrates (glycans) have long been known to play major metabolic, structural and physical roles in biological systems. Targeted microbial binding to host glycans has also been studied for decades. But such biological roles can only explain some of the remarkable complexity and organismal diversity of glycans in nature. Reviewing the subject about two decades ago, one could find very few clear-cut instances of glycan-recognition-specific biological roles of glycans that were of intrinsic value to the organism expressing them. In striking contrast there is now a profusion of examples, such that this updated review cannot be comprehensive. Instead, a historical overview is presented, broad principles outlined and a few examples cited, representing diverse types of roles, mediated by various glycan classes, in different evolutionary lineages. What remains unchanged is the fact that while all theories regarding biological roles of glycans are supported by compelling evidence, exceptions to each can be found. In retrospect, this is not surprising. Complex and diverse glycans appear to be ubiquitous to all cells in nature, and essential to all life forms. Thus, >3 billion years of evolution consistently generated organisms that use these molecules for many key biological roles, even while sometimes coopting them for minor functions. In this respect, glycans are no different from other major macromolecular building blocks of life (nucleic acids, proteins and lipids), simply more rapidly evolving and complex. It is time for the diverse functional roles of glycans to be fully incorporated into the mainstream of biological sciences. PMID:27558841

Integration of systems biology with organs-on-chips to humanize therapeutic development

NASA Astrophysics Data System (ADS)

Edington, Collin D.; Cirit, Murat; Chen, Wen Li Kelly; Clark, Amanda M.; Wells, Alan; Trumper, David L.; Griffith, Linda G.

2017-02-01

"Mice are not little people" - a refrain becoming louder as the gaps between animal models and human disease become more apparent. At the same time, three emerging approaches are headed toward integration: powerful systems biology analysis of cell-cell and intracellular signaling networks in patient-derived samples; 3D tissue engineered models of human organ systems, often made from stem cells; and micro-fluidic and meso-fluidic devices that enable living systems to be sustained, perturbed and analyzed for weeks in culture. Integration of these rapidly moving fields has the potential to revolutionize development of therapeutics for complex, chronic diseases, including those that have weak genetic bases and substantial contributions from gene-environment interactions. Technical challenges in modeling complex diseases with "organs on chips" approaches include the need for relatively large tissue masses and organ-organ cross talk to capture systemic effects, such that current microfluidic formats often fail to capture the required scale and complexity for interconnected systems. These constraints drive development of new strategies for designing in vitro models, including perfusing organ models, as well as "mesofluidic" pumping and circulation in platforms connecting several organ systems, to achieve the appropriate physiological relevance.

Complex Homology and the Evolution of Nervous Systems.

PubMed

Liebeskind, Benjamin J; Hillis, David M; Zakon, Harold H; Hofmann, Hans A

2016-02-01

We examine the complex evolution of animal nervous systems and discuss the ramifications of this complexity for inferring the nature of early animals. Although reconstructing the origins of nervous systems remains a central challenge in biology, and the phenotypic complexity of early animals remains controversial, a compelling picture is emerging. We now know that the nervous system and other key animal innovations contain a large degree of homoplasy, at least on the molecular level. Conflicting hypotheses about early nervous system evolution are due primarily to differences in the interpretation of this homoplasy. We highlight the need for explicit discussion of assumptions and discuss the limitations of current approaches for inferring ancient phenotypic states. Copyright © 2015. Published by Elsevier Ltd.

Systems biomarkers as acute diagnostics and chronic monitoring tools for traumatic brain injury

NASA Astrophysics Data System (ADS)

Wang, Kevin K. W.; Moghieb, Ahmed; Yang, Zhihui; Zhang, Zhiqun

2013-05-01

Traumatic brain injury (TBI) is a significant biomedical problem among military personnel and civilians. There exists an urgent need to develop and refine biological measures of acute brain injury and chronic recovery after brain injury. Such measures "biomarkers" can assist clinicians in helping to define and refine the recovery process and developing treatment paradigms for the acutely injured to reduce secondary injury processes. Recent biomarker studies in the acute phase of TBI have highlighted the importance and feasibilities of identifying clinically useful biomarkers. However, much less is known about the subacute and chronic phases of TBI. We propose here that for a complex biological problem such as TBI, multiple biomarker types might be needed to harness the wide range of pathological and systemic perturbations following injuries, including acute neuronal death, neuroinflammation, neurodegeneration and neuroregeneration to systemic responses. In terms of biomarker types, they range from brain-specific proteins, microRNA, genetic polymorphism, inflammatory cytokines and autoimmune markers and neuro-endocrine hormones. Furthermore, systems biology-driven biomarkers integration can help present a holistic approach to understanding scenarios and complexity pathways involved in brain injury.

Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach.

PubMed

Perez-Acle, Tomas; Fuenzalida, Ignacio; Martin, Alberto J M; Santibañez, Rodrigo; Avaria, Rodrigo; Bernardin, Alejandro; Bustos, Alvaro M; Garrido, Daniel; Dushoff, Jonathan; Liu, James H

2018-03-29

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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.

UML as a cell and biochemistry modeling language.

PubMed

Webb, Ken; White, Tony

2005-06-01

The systems biology community is building increasingly complex models and simulations of cells and other biological entities, and are beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). The lessons learned over the years by the software development community in designing and building increasingly complex telecommunication and other commercial real-time reactive systems, can be advantageously applied to the problems of modeling in the biology domain. Making use of the object-oriented (OO) paradigm, the unified modeling language (UML) and Real-Time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we describe a multi-step process we have used to construct top-down models of cells and cell aggregates. The simple example model described in this paper includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the relevance of abstraction, reuse, objects, classes, component and inheritance hierarchies, multiplicity, visual modeling, and other current software development best practices. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer. We discuss our CellAK (Cell Assembly Kit) approach in terms of features found in SBML, CellML, E-CELL, Gepasi, Jarnac, StochSim, Virtual Cell, and membrane computing systems.

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.

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.

Ionic interactions in biological and physical systems: a variational treatment.

PubMed

Eisenberg, Bob

2013-01-01

Chemistry is about chemical reactions. Chemistry is about electrons changing their configurations as atoms and molecules react. Chemistry has for more than a century studied reactions as if they occurred in ideal conditions of infinitely dilute solutions. But most reactions occur in salt solutions that are not ideal. In those solutions everything (charged) interacts with everything else (charged) through the electric field, which is short and long range extending to the boundaries of the system. Mathematics has recently been developed to deal with interacting systems of this sort. The variational theory of complex fluids has spawned the theory of liquid crystals (or vice versa). In my view, ionic solutions should be viewed as complex fluids, particularly in the biological and engineering context. In both biology and electrochemistry ionic solutions are mixtures highly concentrated (to approximately 10 M) where they are most important, near electrodes, nucleic ids, proteins, active sites of enzymes, and ionic channels. Ca2+ is always involved in biological solutions because the concentration (really free energy per mole) of Ca2+ in a particular location is the signal that controls many biological functions. Such interacting systems are not simple fluids, and it is no wonder that analysis of interactions, such as the Hofmeister series, rooted in that tradition has not succeeded as one would hope. Here, we present a variational treatment of ard spheres in a frictional dielectric with the hope that such a treatment of an lectrolyte as a complex fluid will be productive. The theory automatically extends to spatially nonuniform boundary conditions and the nonequilibrium systems and flows they produce. The theory is unavoidably self-consistent since differential equations are derived (not assumed) from models of (Helmholtz free) nergy and dissipation of the electrolyte. The origin of the Hofmeister series is (in my view) an inverse problem that becomes well posed when enough data from disjoint experimental traditions are interpreted with a self-consistent theory.

Challenges and Rewards on the Road to Translational Systems Biology in Acute Illness: Four Case Reports from Interdisciplinary Teams

PubMed Central

An, Gary; Hunt, C. Anthony; Clermont, Gilles; Neugebauer, Edmund; Vodovotz, Yoram

2007-01-01

Introduction Translational systems biology approaches can be distinguished from mainstream systems biology in that their goal is to drive novel therapies and streamline clinical trials in critical illness. One systems biology approach, dynamic mathematical modeling (DMM), is increasingly used in dealing with the complexity of the inflammatory response and organ dysfunction. The use of DMM often requires a broadening of research methods and a multidisciplinary team approach that includes bioscientists, mathematicians, engineers, and computer scientists. However, the development of these groups must overcome domain-specific barriers to communication and understanding. Methods We present four case studies of successful translational, interdisciplinary systems biology efforts, which differ by organizational level from an individual to an entire research community. Results Case 1 is a single investigator involved in DMM of the acute inflammatory response at Cook County Hospital, in which extensive translational progress was made using agent-based models of inflammation and organ damage. Case 2 is a community-level effort from the University of Witten-Herdecke in Cologne, whose efforts have led to the formation of the Society for Complexity in Acute Illness. Case 3 is an institution-based group, the Biosystems Group at the University of California, San Francisco, whose work has included a focus on a common lexicon for DMM. Case 4 is an institution-based, trans-disciplinary research group (the Center for Inflammation and Regenerative Modeling at the University of Pittsburgh, whose modeling work has led to internal education efforts, grant support, and commercialization. Conclusion A transdisciplinary approach, which involves team interaction in an iterative fashion to address ambiguity and is supported by educational initiatives, is likely to be necessary for DMM in acute illness. Community-wide organizations such as the Society of Complexity in Acute Illness (SCAI) must strive to facilitate the implementation of DMM in sepsis/trauma research into the research community as a whole. PMID:17548029

The diverse and expanding role of mass spectrometry in structural and molecular biology.

PubMed

Lössl, Philip; van de Waterbeemd, Michiel; Heck, Albert Jr

2016-12-15

The emergence of proteomics has led to major technological advances in mass spectrometry (MS). These advancements not only benefitted MS-based high-throughput proteomics but also increased the impact of mass spectrometry on the field of structural and molecular biology. Here, we review how state-of-the-art MS methods, including native MS, top-down protein sequencing, cross-linking-MS, and hydrogen-deuterium exchange-MS, nowadays enable the characterization of biomolecular structures, functions, and interactions. In particular, we focus on the role of mass spectrometry in integrated structural and molecular biology investigations of biological macromolecular complexes and cellular machineries, highlighting work on CRISPR-Cas systems and eukaryotic transcription complexes. © 2016 The Authors. Published under the terms of the CC BY NC ND 4.0 license.

MECHANISTIC INDICATORS OF CHILDHOOD ASTHMA (MICA): A SYSTEMS BIOLOGY APPROACH FOR THE INTEGRATION OF MULTIFACTORIAL EXPOSURE AND ENVIRONMENTAL HEALTH DATA

EPA Science Inventory

Modem methods in molecular biology and advanced computational tools show promise in elucidating complex interactions that occur between genes and environmental factors in diseases such as asthma. However, appropriately designed studies are critical for these methods to reach the...

A subcontinental view of forest plant invasions

Treesearch

Christopher M. Oswalt; Songlin Fei; Qinfeng Guo; Basil V. Iannone III; Sonja N. Oswalt; Bryan C. Pijanowski; Kevin M. Potter

2015-01-01

Over the last few decades, considerable attention has focused on small-scale studies of invasive plants and invaded systems. Unfortunately, small scale studies rarely provide comprehensive insight into the complexities of biological invasions at macroscales. Systematic and repeated monitoring of biological invasions at broad scales are rare. In this report, we...

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

A System Biology Perspective on Environment-Host-Microbe Interactions.

PubMed

Chen, Lianmin; Garmaeva, Sanzhima; Zherankova, Alexandra; Fu, Jingyuan; Wijmenga, Cisca

2018-04-16

A vast, complex and dynamic consortium of microorganisms known as the gut microbiome colonizes the human gut. Over the past few decades we have developed an increased awareness of its important role in human health. In this review we discuss the role of the gut microbiome in complex diseases and the possible causal scenarios behind its interactions with the host genome and environmental factors. We then propose a new analysis framework that combines a systems biology approach, cross-kingdom integration of multiple levels of omics data, and innovative in vitro models to yield an integrated picture of human host-microbe interactions. This new framework will lay the foundation for the development of the next phase in personalized medicine.

Molecular dynamics simulations of large macromolecular complexes.

PubMed

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

2015-04-01

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

Dancing with Swarms: Utilizing Swarm Intelligence to Build, Investigate, and Control Complex Systems

NASA Astrophysics Data System (ADS)

Jacob, Christian

We are surrounded by a natural world of massively parallel, decentralized biological "information processing" systems, a world that exhibits fascinating emergent properties in many ways. In fact, our very own bodies are the result of emergent patterns, as the development of any multi-cellular organism is determined by localized interactions among an enormous number of cells, carefully orchestrated by enzymes, signalling proteins and other molecular "agents". What is particularly striking about these highly distributed developmental processes is that a centralized control agency is completely absent. This is also the case for many other biological systems, such as termites which build their nests—without an architect that draws a plan, or brain cells evolving into a complex `mind machine'—without an explicit blueprint of a network layout.

Functional complexity and ecosystem stability: an experimental approach

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

Van Voris, P.; O'Neill, R.V.; Shugart, H.H.

1978-01-01

The complexity-stability hypothesis was experimentally tested using intact terrestrial microcosms. Functional complexity was defined as the number and significance of component interactions (i.e., population interactions, physical-chemical reactions, biological turnover rates) influenced by nonlinearities, feedbacks, and time delays. It was postulated that functional complexity could be nondestructively measured through analysis of a signal generated from the system. Power spectral analysis of hourly CO/sub 2/ efflux, from eleven old-field microcosms, was analyzed for the number of low frequency peaks and used to rank the functional complexity of each system. Ranking of ecosystem stability was based on the capacity of the system tomore » retain essential nutrients and was measured by net loss of Ca after the system was stressed. Rank correlation supported the hypothesis that increasing ecosystem functional complexity leads to increasing ecosystem stability. The results indicated that complex functional dynamics can serve to stabilize the system. The results also demonstrated that microcosms are useful tools for system-level investigations.« less

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

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling.

PubMed

Sumner, T; Shephard, E; Bogle, I D L

2012-09-07

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

Accessing Nature’s diversity through metabolic engineering and synthetic biology

PubMed Central

King, Jason R.; Edgar, Steven; Qiao, Kangjian; Stephanopoulos, Gregory

2016-01-01

In this perspective, we highlight recent examples and trends in metabolic engineering and synthetic biology that demonstrate the synthetic potential of enzyme and pathway engineering for natural product discovery. In doing so, we introduce natural paradigms of secondary metabolism whereby simple carbon substrates are combined into complex molecules through “scaffold diversification”, and subsequent “derivatization” of these scaffolds is used to synthesize distinct complex natural products. We provide examples in which modern pathway engineering efforts including combinatorial biosynthesis and biological retrosynthesis can be coupled to directed enzyme evolution and rational enzyme engineering to allow access to the “privileged” chemical space of natural products in industry-proven microbes. Finally, we forecast the potential to produce natural product-like discovery platforms in biological systems that are amenable to single-step discovery, validation, and synthesis for streamlined discovery and production of biologically active agents. PMID:27081481

Small-angle neutron scattering reveals the assembly mode and oligomeric architecture of TET, a large, dodecameric aminopeptidase

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

Appolaire, Alexandre; Girard, Eric; Colombo, Matteo

2014-11-01

The present work illustrates that small-angle neutron scattering, deuteration and contrast variation, combined with in vitro particle reconstruction, constitutes a very efficient approach to determine subunit architectures in large, symmetric protein complexes. In the case of the 468 kDa heterododecameric TET peptidase machine, it was demonstrated that the assembly of the 12 subunits is a highly controlled process and represents a way to optimize the catalytic efficiency of the enzyme. The specific self-association of proteins into oligomeric complexes is a common phenomenon in biological systems to optimize and regulate their function. However, de novo structure determination of these important complexesmore » is often very challenging for atomic-resolution techniques. Furthermore, in the case of homo-oligomeric complexes, or complexes with very similar building blocks, the respective positions of subunits and their assembly pathways are difficult to determine using many structural biology techniques. Here, an elegant and powerful approach based on small-angle neutron scattering is applied, in combination with deuterium labelling and contrast variation, to elucidate the oligomeric organization of the quaternary structure and the assembly pathways of 468 kDa, hetero-oligomeric and symmetric Pyrococcus horikoshii TET2–TET3 aminopeptidase complexes. The results reveal that the topology of the PhTET2 and PhTET3 dimeric building blocks within the complexes is not casual but rather suggests that their quaternary arrangement optimizes the catalytic efficiency towards peptide substrates. This approach bears important potential for the determination of quaternary structures and assembly pathways of large oligomeric and symmetric complexes in biological systems.« less

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

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.

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

Complex systems in metabolic engineering.

PubMed

Winkler, James D; Erickson, Keesha; Choudhury, Alaksh; Halweg-Edwards, Andrea L; Gill, Ryan T

2015-12-01

Metabolic engineers manipulate intricate biological networks to build efficient biological machines. The inherent complexity of this task, derived from the extensive and often unknown interconnectivity between and within these networks, often prevents researchers from achieving desired performance. Other fields have developed methods to tackle the issue of complexity for their unique subset of engineering problems, but to date, there has not been extensive and comprehensive examination of how metabolic engineers use existing tools to ameliorate this effect on their own research projects. In this review, we examine how complexity affects engineering at the protein, pathway, and genome levels within an organism, and the tools for handling these issues to achieve high-performing strain designs. Quantitative complexity metrics and their applications to metabolic engineering versus traditional engineering fields are also discussed. We conclude by predicting how metabolic engineering practices may advance in light of an explicit consideration of design complexity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Newton, Laplace, and The Epistemology of Systems Biology

PubMed Central

Bittner, Michael L.; Dougherty, Edward R.

2012-01-01

For science, theoretical or applied, to significantly advance, researchers must use the most appropriate mathematical methods. A century and a half elapsed between Newton’s development of the calculus and Laplace’s development of celestial mechanics. One cannot imagine the latter without the former. Today, more than three-quarters of a century has elapsed since the birth of stochastic systems theory. This article provides a perspective on the utilization of systems theory as the proper vehicle for the development of systems biology and its application to complex regulatory diseases such as cancer. PMID:23170064

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.

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…

A Macro-Level Analysis of SRL Processes and Their Relations to the Acquisition of a Sophisticated Mental Model of a Complex System

ERIC Educational Resources Information Center

Greene, Jeffrey Alan; Azevedo, Roger

2009-01-01

In this study, we used think-aloud verbal protocols to examine how various macro-level processes of self-regulated learning (SRL; e.g., planning, monitoring, strategy use, handling of task difficulty and demands) were associated with the acquisition of a sophisticated mental model of a complex biological system. Numerous studies examine how…

Principal process analysis of biological models.

PubMed

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

2018-06-14

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

A Chemical Engineer's Perspective on Health and Disease

PubMed Central

Androulakis, Ioannis P.

2014-01-01

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems. PMID:25506103

Apparatus and methods for manipulation and optimization of biological systems

NASA Technical Reports Server (NTRS)

Sun, Ren (Inventor); Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Yu, Fuqu (Inventor)

2012-01-01

The invention provides systems and methods for manipulating, e.g., optimizing and controlling, biological systems, e.g., for eliciting a more desired biological response of biological sample, such as a tissue, organ, and/or a cell. In one aspect, systems and methods of the invention operate by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system, e.g., a bioreactor. In alternative aspects, systems include a device for sustaining cells or tissue samples, one or more actuators for stimulating the samples via biochemical, electromagnetic, thermal, mechanical, and/or optical stimulation, one or more sensors for measuring a biological response signal of the samples resulting from the stimulation of the sample. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The compositions and methods of the invention can be used, e.g., to for systems optimization of any biological manufacturing or experimental system, e.g., bioreactors for proteins, e.g., therapeutic proteins, polypeptides or peptides for vaccines, and the like, small molecules (e.g., antibiotics), polysaccharides, lipids, and the like. Another use of the apparatus and methods includes combination drug therapy, e.g. optimal drug cocktail, directed cell proliferations and differentiations, e.g. in tissue engineering, e.g. neural progenitor cells differentiation, and discovery of key parameters in complex biological systems.

Evolutionary Models for Simple Biosystems

NASA Astrophysics Data System (ADS)

Bagnoli, Franco

The concept of evolutionary development of structures constituted a real revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.

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.

Systems biology impact on antiepileptic drug discovery.

PubMed

Margineanu, Doru Georg

2012-02-01

Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs. Copyright © 2011 Elsevier B.V. All rights reserved.

Biomedically relevant circuit-design strategies in mammalian synthetic biology

PubMed Central

Bacchus, William; Aubel, Dominique; Fussenegger, Martin

2013-01-01

The development and progress in synthetic biology has been remarkable. Although still in its infancy, synthetic biology has achieved much during the past decade. Improvements in genetic circuit design have increased the potential for clinical applicability of synthetic biology research. What began as simple transcriptional gene switches has rapidly developed into a variety of complex regulatory circuits based on the transcriptional, translational and post-translational regulation. Instead of compounds with potential pharmacologic side effects, the inducer molecules now used are metabolites of the human body and even members of native cell signaling pathways. In this review, we address recent progress in mammalian synthetic biology circuit design and focus on how novel designs push synthetic biology toward clinical implementation. Groundbreaking research on the implementation of optogenetics and intercellular communications is addressed, as particularly optogenetics provides unprecedented opportunities for clinical application. Along with an increase in synthetic network complexity, multicellular systems are now being used to provide a platform for next-generation circuit design. PMID:24061539

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.

Systemic Darwinism

PubMed Central

Winther, Rasmus Grønfeldt

2008-01-01

Darwin's 19th century evolutionary theory of descent with modification through natural selection opened up a multidimensional and integrative conceptual space for biology. We explore three dimensions of this space: explanatory pattern, levels of selection, and degree of difference among units of the same type. Each dimension is defined by a respective pair of poles: law and narrative explanation, organismic and hierarchical selection, and variational and essentialist thinking. As a consequence of conceptual debates in the 20th century biological sciences, the poles of each pair came to be seen as mutually exclusive opposites. A significant amount of 21st century research focuses on systems (e.g., genomic, cellular, organismic, and ecological/global). Systemic Darwinism is emerging in this context. It follows a “compositional paradigm” according to which complex systems and their hierarchical networks of parts are the focus of biological investigation. Through the investigation of systems, Systemic Darwinism promises to reintegrate each dimension of Darwin's original logical space. Moreover, this ideally and potentially unified theory of biological ontology coordinates and integrates a plurality of mathematical biological theories (e.g., self-organization/structure, cladistics/history, and evolutionary genetics/function). Integrative Systemic Darwinism requires communal articulation from a plurality of perspectives. Although it is more general than these, it draws on previous advances in Systems Theory, Systems Biology, and Hierarchy Theory. Systemic Darwinism would greatly further bioengineering research and would provide a significantly deeper and more critical understanding of biological reality. PMID:18697926

Systemic darwinism.

PubMed

Winther, Rasmus Grønfeldt

2008-08-19

Darwin's 19th century evolutionary theory of descent with modification through natural selection opened up a multidimensional and integrative conceptual space for biology. We explore three dimensions of this space: explanatory pattern, levels of selection, and degree of difference among units of the same type. Each dimension is defined by a respective pair of poles: law and narrative explanation, organismic and hierarchical selection, and variational and essentialist thinking. As a consequence of conceptual debates in the 20th century biological sciences, the poles of each pair came to be seen as mutually exclusive opposites. A significant amount of 21st century research focuses on systems (e.g., genomic, cellular, organismic, and ecological/global). Systemic Darwinism is emerging in this context. It follows a "compositional paradigm" according to which complex systems and their hierarchical networks of parts are the focus of biological investigation. Through the investigation of systems, Systemic Darwinism promises to reintegrate each dimension of Darwin's original logical space. Moreover, this ideally and potentially unified theory of biological ontology coordinates and integrates a plurality of mathematical biological theories (e.g., self-organization/structure, cladistics/history, and evolutionary genetics/function). Integrative Systemic Darwinism requires communal articulation from a plurality of perspectives. Although it is more general than these, it draws on previous advances in Systems Theory, Systems Biology, and Hierarchy Theory. Systemic Darwinism would greatly further bioengineering research and would provide a significantly deeper and more critical understanding of biological reality.

HYDROGEOLOGIC SETTING AND CHARACTERISTICS OF RIPARIAN MEADOW COMPLEXES IN THE MOUNTAINS OF CENTRAL NEVADA: A CASE STUDY

EPA Science Inventory

Riparian wet meadow complexes in the mountains of the central Great Basin are scarce, ecologically important systems threatened by stream incision. An interdisciplinary team from government and academia is investigating the origin, setting, and biological--physical interrelations...

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.

Model identification of signal transduction networks from data using a state regulator problem.

PubMed

Gadkar, K G; Varner, J; Doyle, F J

2005-03-01

Advances in molecular biology provide an opportunity to develop detailed models of biological processes that can be used to obtain an integrated understanding of the system. However, development of useful models from the available knowledge of the system and experimental observations still remains a daunting task. In this work, a model identification strategy for complex biological networks is proposed. The approach includes a state regulator problem (SRP) that provides estimates of all the component concentrations and the reaction rates of the network using the available measurements. The full set of the estimates is utilised for model parameter identification for the network of known topology. An a priori model complexity test that indicates the feasibility of performance of the proposed algorithm is developed. Fisher information matrix (FIM) theory is used to address model identifiability issues. Two signalling pathway case studies, the caspase function in apoptosis and the MAP kinase cascade system, are considered. The MAP kinase cascade, with measurements restricted to protein complex concentrations, fails the a priori test and the SRP estimates are poor as expected. The apoptosis network structure used in this work has moderate complexity and is suitable for application of the proposed tools. Using a measurement set of seven protein concentrations, accurate estimates for all unknowns are obtained. Furthermore, the effects of measurement sampling frequency and quality of information in the measurement set on the performance of the identified model are described.

Chemistry meets biology in colitis-associated carcinogenesis

PubMed Central

Mangerich, Aswin; Dedon, Peter C.; Fox, James G.; Tannenbaum, Steven R.; Wogan, Gerald N.

2015-01-01

The intestine comprises an exceptional venue for a dynamic and complex interplay of numerous chemical and biological processes. Here, multiple chemical and biological systems, including the intestinal tissue itself, its associated immune system, the gut microbiota, xenobiotics, and metabolites meet and interact to form a sophisticated and tightly regulated state of tissue homoeostasis. Disturbance of this homeostasis can cause inflammatory bowel disease (IBD) – a chronic disease of multifactorial etiology that is strongly associated with increased risk for cancer development. This review addresses recent developments in research into chemical and biological mechanisms underlying the etiology of inflammation-induced colon cancer. Beginning with a general overview of reactive chemical species generated during colonic inflammation, the mechanistic interplay between chemical and biological mediators of inflammation, the role of genetic toxicology and microbial pathogenesis in disease development are discussed. When possible, we systematically compare evidence from studies utilizing human IBD patients with experimental investigations in mice. The comparison reveals that many strong pathological and mechanistic correlates exist between mouse models of colitis-associated cancer, and the clinically relevant situation in humans. We also summarize several emerging issues in the field, such as the carcinogenic potential of novel inflammation-related DNA adducts and genotoxic microbial factors, the systemic dimension of inflammation-induced genotoxicity, and the complex role of genome maintenance mechanisms during these processes. Taken together, current evidence points to the induction of genetic and epigenetic alterations by chemical and biological inflammatory stimuli ultimately leading to cancer formation. PMID:23926919

Artificial cell mimics as simplified models for the study of cell biology.

PubMed

Salehi-Reyhani, Ali; Ces, Oscar; Elani, Yuval

2017-07-01

Living cells are hugely complex chemical systems composed of a milieu of distinct chemical species (including DNA, proteins, lipids, and metabolites) interconnected with one another through a vast web of interactions: this complexity renders the study of cell biology in a quantitative and systematic manner a difficult task. There has been an increasing drive towards the utilization of artificial cells as cell mimics to alleviate this, a development that has been aided by recent advances in artificial cell construction. Cell mimics are simplified cell-like structures, composed from the bottom-up with precisely defined and tunable compositions. They allow specific facets of cell biology to be studied in isolation, in a simplified environment where control of variables can be achieved without interference from a living and responsive cell. This mini-review outlines the core principles of this approach and surveys recent key investigations that use cell mimics to address a wide range of biological questions. It will also place the field in the context of emerging trends, discuss the associated limitations, and outline future directions of the field. Impact statement Recent years have seen an increasing drive to construct cell mimics and use them as simplified experimental models to replicate and understand biological phenomena in a well-defined and controlled system. By summarizing the advances in this burgeoning field, and using case studies as a basis for discussion on the limitations and future directions of this approach, it is hoped that this minireview will spur others in the experimental biology community to use artificial cells as simplified models with which to probe biological systems.

Characterisation of minimalist co-assembled fluorenylmethyloxycarbonyl self-assembling peptide systems for presentation of multiple bioactive peptides.

PubMed

Horgan, Conor C; Rodriguez, Alexandra L; Li, Rui; Bruggeman, Kiara F; Stupka, Nicole; Raynes, Jared K; Day, Li; White, John W; Williams, Richard J; Nisbet, David R

2016-07-01

The nanofibrillar structures that underpin self-assembling peptide (SAP) hydrogels offer great potential for the development of finely tuned cellular microenvironments suitable for tissue engineering. However, biofunctionalisation without disruption of the assembly remains a key issue. SAPS present the peptide sequence within their structure, and studies to date have typically focused on including a single biological motif, resulting in chemically and biologically homogenous scaffolds. This limits the utility of these systems, as they cannot effectively mimic the complexity of the multicomponent extracellular matrix (ECM). In this work, we demonstrate the first successful co-assembly of two biologically active SAPs to form a coassembled scaffold of distinct two-component nanofibrils, and demonstrate that this approach is more bioactive than either of the individual systems alone. Here, we use two bioinspired SAPs from two key ECM proteins: Fmoc-FRGDF containing the RGD sequence from fibronectin and Fmoc-DIKVAV containing the IKVAV sequence from laminin. Our results demonstrate that these SAPs are able to co-assemble to form stable hybrid nanofibres containing dual epitopes. Comparison of the co-assembled SAP system to the individual SAP hydrogels and to a mixed system (composed of the two hydrogels mixed together post-assembly) demonstrates its superior stable, transparent, shear-thinning hydrogels at biological pH, ideal characteristics for tissue engineering applications. Importantly, we show that only the coassembled hydrogel is able to induce in vitro multinucleate myotube formation with C2C12 cells. This work illustrates the importance of tissue engineering scaffold functionalisation and the need to develop increasingly advanced multicomponent systems for effective ECM mimicry. Successful control of stem cell fate in tissue engineering applications requires the use of sophisticated scaffolds that deliver biological signals to guide growth and differentiation. The complexity of such processes necessitates the presentation of multiple signals in order to effectively mimic the native extracellular matrix (ECM). Here, we establish the use of two biofunctional, minimalist self-assembling peptides (SAPs) to construct the first co-assembled SAP scaffold. Our work characterises this construct, demonstrating that the physical, chemical, and biological properties of the peptides are maintained during the co-assembly process. Importantly, the coassembled system demonstrates superior biological performance relative to the individual SAPs, highlighting the importance of complex ECM mimicry. This work has important implications for future tissue engineering studies. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Complexity and robustness

PubMed Central

Carlson, J. M.; Doyle, John

2002-01-01

Highly optimized tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity. HOT is motivated primarily by systems from biology and engineering and emphasizes, (i) highly structured, nongeneric, self-dissimilar internal configurations, and (ii) robust yet fragile external behavior. HOT claims these are the most important features of complexity and not accidents of evolution or artifices of engineering design but are inevitably intertwined and mutually reinforcing. In the spirit of this collection, our paper contrasts HOT with alternative perspectives on complexity, drawing on real-world examples and also model systems, particularly those from self-organized criticality. PMID:11875207

Has Neo-Darwinism failed clinical medicine: does systems biology have to?

PubMed

Joyner, Michael J

2015-01-01

In this essay I argue that Neo-Darwinism ultimately led to an oversimplified genotype equals phenotype view of human disease. This view has been called into question by the unexpected results of the Human Genome Project which has painted a far more complex picture of the genetic features of human disease than was anticipated. Cell centric Systems Biology is now attempting to reconcile this complexity. However, it too is limited because most common chronic diseases have systemic components not predicted by their intracellular responses alone. In this context, congestive heart failure is a classic example of this general problem and I discuss it as a systemic disease vs. one solely related to dysfunctional cardiomyocytes. I close by arguing that a physiological perspective is essential to reconcile reductionism with what is required to understand and treat disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Environmental and Biological Factors Influencing Culex pipiens quinquefasciatus Say (Diptera: Culicidae) Vector Competence for Saint Louis Encephalitis Virus

PubMed Central

Richards, Stephanie L.; Lord, Cynthia C.; Pesko, Kendra; Tabachnick, Walter J.

2009-01-01

Complex interactions between environmental and biological factors influence the susceptibility of Culex pipiens quinquefasciatus to St. Louis encephalitis virus and could affect the epidemiology of virus transmission. Similar interactions could have epidemiologic implications for other vector-virus systems. We conducted an experiment to examine four such factors in combination: mosquito age, extrinsic incubation temperature (EIT), virus dose, and colony. The proportion of mosquitoes with body infections or disseminated infections varied between colonies, and was dependant on age, EIT, and dose. We also show that the probability of a body or leg infection interacted in complex ways between colonies, ages, EITs, and doses. The complex interactive effects of environmental and biological factors must be taken into account for studies of vector competence and epidemiology, especially when laboratory studies are used to generalize to natural transmission dynamics where the extent of variation is largely unknown. PMID:19635881

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.

DOT2: Macromolecular Docking With Improved Biophysical Models

PubMed Central

Roberts, Victoria A.; Thompson, Elaine E.; Pique, Michael E.; Perez, Martin S.; Eyck, Lynn Ten

2015-01-01

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate a complete list of favorable candidate configu-rations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions, and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome. PMID:23695987

Environmental and biological factors influencing Culex pipiens quinquefasciatus Say (Diptera: Culicidae) vector competence for Saint Louis encephalitis virus.

PubMed

Richards, Stephanie L; Lord, Cynthia C; Pesko, Kendra; Tabachnick, Walter J

2009-08-01

Complex interactions between environmental and biological factors influence the susceptibility of Culex pipiens quinquefasciatus to St. Louis encephalitis virus and could affect the epidemiology of virus transmission. Similar interactions could have epidemiologic implications for other vector-virus systems. We conducted an experiment to examine four such factors in combination: mosquito age, extrinsic incubation temperature (EIT), virus dose, and colony. The proportion of mosquitoes with body infections or disseminated infections varied between colonies, and was dependant on age, EIT, and dose. We also show that the probability of a body or leg infection interacted in complex ways between colonies, ages, EITs, and doses. The complex interactive effects of environmental and biological factors must be taken into account for studies of vector competence and epidemiology, especially when laboratory studies are used to generalize to natural transmission dynamics where the extent of variation is largely unknown.

Phenomenon of statistical instability of the third type systems—complexity

NASA Astrophysics Data System (ADS)

Eskov, V. V.; Gavrilenko, T. V.; Eskov, V. M.; Vokhmina, Yu. V.

2017-11-01

The problem of the existence and special properties of third type systems has been formulated within the new chaos-self-organization theory. In fact, a global problem of the possibility of the existence of steady-state regimes for homeostatic systems has been considered. These systems include not only medical and biological systems, but also the dynamics of meteorological parameters, as well as the ambient parameters of the environment in which humans are located. The new approach has been used to give a new definition for homeostatic systems (complexity).

Data based identification and prediction of nonlinear and complex dynamical systems

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

2016-07-01

The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The "inverse" problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear dynamical systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and complex systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and complex networked systems. The problems include identification of chaotic systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear systems, reconstruction of complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of complex geospatial networks and nodal positioning, and reconstruction of complex spreading networks with binary data.. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of complex nonlinear dynamical networks are discussed. The methods outlined in this Review are principled on various concepts in complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks toward the ultimate goal of controlling such systems.

Biological invasions on oceanic islands: Implications for island ecosystems and avifauna

Treesearch

Dean E. Pearson

2009-01-01

Biological invasions present a global threat to biodiversity, but oceanic islands are the systems hardest hit by invasions. Islands are generally depauperate in species richness, trophic complexity, and functional diversity relative to comparable mainland ecosystems. This situation results in low biotic resistance to invasion and many empty niches for invaders to...

A biochemical network can control formation of a synthetic material by sensing numerous specific stimuli

NASA Astrophysics Data System (ADS)

Hun Yeon, Ju; Chan, Karen Y. T.; Wong, Ting-Chia; Chan, Kelvin; Sutherland, Michael R.; Ismagilov, Rustem F.; Pryzdial, Edward L. G.; Kastrup, Christian J.

2015-05-01

Developing bio-compatible smart materials that assemble in response to environmental cues requires strategies that can discriminate multiple specific stimuli in a complex milieu. Synthetic materials have yet to achieve this level of sensitivity, which would emulate the highly evolved and tailored reaction networks of complex biological systems. Here we show that the output of a naturally occurring network can be replaced with a synthetic material. Exploiting the blood coagulation system as an exquisite biological sensor, the fibrin clot end-product was replaced with a synthetic material under the biological control of a precisely regulated cross-linking enzyme. The functions of the coagulation network remained intact when the material was incorporated. Clot-like polymerization was induced in indirect response to distinct small molecules, phospholipids, enzymes, cells, viruses, an inorganic solid, a polyphenol, a polysaccharide, and a membrane protein. This strategy demonstrates for the first time that an existing stimulus-responsive biological network can be used to control the formation of a synthetic material by diverse classes of physiological triggers.

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.

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

Biosynthetic inorganic chemistry.

PubMed

Lu, Yi

2006-08-25

Inorganic chemistry and biology can benefit greatly from each other. Although synthetic and physical inorganic chemistry have been greatly successful in clarifying the role of metal ions in biological systems, the time may now be right to utilize biological systems to advance coordination chemistry. One such example is the use of small, stable, easy-to-make, and well-characterized proteins as ligands to synthesize novel inorganic compounds. This biosynthetic inorganic chemistry is possible thanks to a number of developments in biology. This review summarizes the progress in the synthesis of close models of complex metalloproteins, followed by a description of recent advances in using the approach for making novel compounds that are unprecedented in either inorganic chemistry or biology. The focus is mainly on synthetic "tricks" learned from biology, as well as novel structures and insights obtained. The advantages and disadvantages of this biosynthetic approach are discussed.

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

PubMed

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

Automated Planning Enables Complex Protocols on Liquid-Handling Robots.

PubMed

Whitehead, Ellis; Rudolf, Fabian; Kaltenbach, Hans-Michael; Stelling, Jörg

2018-03-16

Robotic automation in synthetic biology is especially relevant for liquid handling to facilitate complex experiments. However, research tasks that are not highly standardized are still rarely automated in practice. Two main reasons for this are the substantial investments required to translate molecular biological protocols into robot programs, and the fact that the resulting programs are often too specific to be easily reused and shared. Recent developments of standardized protocols and dedicated programming languages for liquid-handling operations addressed some aspects of ease-of-use and portability of protocols. However, either they focus on simplicity, at the expense of enabling complex protocols, or they entail detailed programming, with corresponding skills and efforts required from the users. To reconcile these trade-offs, we developed Roboliq, a software system that uses artificial intelligence (AI) methods to integrate (i) generic formal, yet intuitive, protocol descriptions, (ii) complete, but usually hidden, programming capabilities, and (iii) user-system interactions to automatically generate executable, optimized robot programs. Roboliq also enables high-level specifications of complex tasks with conditional execution. To demonstrate the system's benefits for experiments that are difficult to perform manually because of their complexity, duration, or time-critical nature, we present three proof-of-principle applications for the reproducible, quantitative characterization of GFP variants.

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.

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

A geometric initial guess for localized electronic orbitals in modular biological systems

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

Beckman, P. G.; Fattebert, J. L.; Lau, E. Y.

Recent first-principles molecular dynamics algorithms using localized electronic orbitals have achieved O(N) complexity and controlled accuracy in simulating systems with finite band gaps. However, accurately deter- mining the centers of these localized orbitals during simulation setup may require O(N 3) operations, which is computationally infeasible for many biological systems. We present an O(N) approach for approximating orbital centers in proteins, DNA, and RNA which uses non-localized solutions for a set of fixed-size subproblems to create a set of geometric maps applicable to larger systems. This scalable approach, used as an initial guess in the O(N) first-principles molecular dynamics code MGmol,more » facilitates first-principles simulations in biological systems of sizes which were previously impossible.« less

Interacting complex systems: Theory and application to real-world situations

NASA Astrophysics Data System (ADS)

Piccinini, Nicola

The interest in complex systems has increased exponentially during the past years because it was found helpful in addressing many of today's challenges. The study of the brain, biology, earthquakes, markets and social sciences are only a few examples of the fields that have benefited from the investigation of complex systems. Internet, the increased mobility of people and the raising energy demand are among the factors that brought in contact complex systems that were isolated till a few years ago. A theory for the interaction between complex systems is becoming more and more urgent to help mankind in this transition. The present work builds upon the most recent results in this field by solving a theoretical problem that prevented previous work to be applied to important complex systems, like the brain. It also shows preliminary laboratory results of perturbation of in vitro neural networks that were done to test the theory. Finally, it gives a preview of the studies that are being done to create a theory that is even closer to the interaction between real complex systems.

COMPLEX HOST-PARASITE SYSTEMS IN MARTES: IMPLICATIONS FOR CONSERVATION BIOLOGY OF ENDEMIC FAUNAS.

USDA-ARS?s Scientific Manuscript database

Complex assemblages of hosts and parasites reveal insights about biogeography and ecology and inform us about processes which serve to structure faunal diversity and the biosphere in space and time. Exploring aspects of parasite diversity among martens (species of Martes) and other mustelids reveal...

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…

Networking at the Protein Society symposium.

PubMed

McKnight, C James; Cordes, Matthew H J

2005-10-01

From the complex behavior of multicomponent signaling networks to the structures of large protein complexes and aggregates, questions once viewed as daunting are now being tackled fearlessly by protein scientists. The 19th Annual Symposium of the Protein Society in Boston highlighted the maturation of systems biology as applied to proteins.

Complexity reduction of biochemical rate expressions.

PubMed

Schmidt, Henning; Madsen, Mads F; Danø, Sune; Cedersund, Gunnar

2008-03-15

The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods. We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation. The method has been implemented in the Systems Biology Toolbox 2 for MATLAB, which is freely available from http://www.sbtoolbox2.org. The Supplementary Material contains scripts that show how to use it by applying the method to the example models, discussed in this article.

Comparative systems biology between human and animal models based on next-generation sequencing methods.

PubMed

Zhao, Yu-Qi; Li, Gong-Hua; Huang, Jing-Fei

2013-04-01

Animal models provide myriad benefits to both experimental and clinical research. Unfortunately, in many situations, they fall short of expected results or provide contradictory results. In part, this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism. To improve the efficacy of animal models, a technological breakthrough is required. The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform. In the present study, we introduce the concept of the comparative systems biology, which we define as "comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels". Furthermore, we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

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.

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

Notable Expressions: Transcriptional Regulation from Biochemistry to Immunology | Center for Cancer Research

Cancer.gov

Dinah Singer, Ph.D., came to NCI in 1975 as a Postdoctoral Fellow in the Laboratory of Biochemistry, but soon created a career for herself in the Experimental Immunology Branch. Her interest in how genes are regulated to control biological function led her to focus on major histocompatibility complex class I genes (MHC Class I)—molecules critical to immune system function—as a model system for complex regulation of ubiquitously expressed genes across cell types and molecular contexts. Using this system to study the sequence elements and factors that control transcription, her laboratory continues to uncover fundamental principles of gene regulation. In addition to her active research career, Singer has served since 1999 as Director of NCI’s Division of Cancer Biology, which manages a portfolio of over 2,200 grants to extramural investigators.

Biomedical text mining and its applications in cancer research.

PubMed

Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

2013-04-01

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

Investigating dye performance and crosstalk in fluorescence enabled bioimaging using a model system

PubMed Central

Arppe, Riikka; Carro-Temboury, Miguel R.; Hempel, Casper; Vosch, Tom

2017-01-01

Detailed imaging of biological structures, often smaller than the diffraction limit, is possible in fluorescence microscopy due to the molecular size and photophysical properties of fluorescent probes. Advances in hardware and multiple providers of high-end bioimaging makes comparing images between studies and between research groups very difficult. Therefore, we suggest a model system to benchmark instrumentation, methods and staining procedures. The system we introduce is based on doped zeolites in stained polyvinyl alcohol (PVA) films: a highly accessible model system which has the properties needed to act as a benchmark in bioimaging experiments. Rather than comparing molecular probes and imaging methods in complicated biological systems, we demonstrate that the model system can emulate this complexity and can be used to probe the effect of concentration, brightness, and cross-talk of fluorophores on the detected fluorescence signal. The described model system comprises of lanthanide (III) ion doped Linde Type A zeolites dispersed in a PVA film stained with fluorophores. We tested: F18, MitoTracker Red and ATTO647N. This model system allowed comparing performance of the fluorophores in experimental conditions. Importantly, we here report considerable cross-talk of the dyes when exchanging excitation and emission settings. Additionally, bleaching was quantified. The proposed model makes it possible to test and benchmark staining procedures before these dyes are applied to more complex biological systems. PMID:29176775

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

Leaf LIMS: A Flexible Laboratory Information Management System with a Synthetic Biology Focus.

PubMed

Craig, Thomas; Holland, Richard; D'Amore, Rosalinda; Johnson, James R; McCue, Hannah V; West, Anthony; Zulkower, Valentin; Tekotte, Hille; Cai, Yizhi; Swan, Daniel; Davey, Robert P; Hertz-Fowler, Christiane; Hall, Anthony; Caddick, Mark

2017-12-15

This paper presents Leaf LIMS, a flexible laboratory information management system (LIMS) designed to address the complexity of synthetic biology workflows. At the project's inception there was a lack of a LIMS designed specifically to address synthetic biology processes, with most systems focused on either next generation sequencing or biobanks and clinical sample handling. Leaf LIMS implements integrated project, item, and laboratory stock tracking, offering complete sample and construct genealogy, materials and lot tracking, and modular assay data capture. Hence, it enables highly configurable task-based workflows and supports data capture from project inception to completion. As such, in addition to it supporting synthetic biology it is ideal for many laboratory environments with multiple projects and users. The system is deployed as a web application through Docker and is provided under a permissive MIT license. It is freely available for download at https://leaflims.github.io .

Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project.

PubMed

Hucka, M; Finney, A; Bornstein, B J; Keating, S M; Shapiro, B E; Matthews, J; Kovitz, B L; Schilstra, M J; Funahashi, A; Doyle, J C; Kitano, H

2004-06-01

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

A Model of Biological Attacks on a Realistic Population

NASA Astrophysics Data System (ADS)

Carley, Kathleen M.; Fridsma, Douglas; Casman, Elizabeth; Altman, Neal; Chen, Li-Chiou; Kaminsky, Boris; Nave, Demian; Yahja, Alex

The capability to assess the impacts of large-scale biological attacks and the efficacy of containment policies is critical and requires knowledge-intensive reasoning about social response and disease transmission within a complex social system. There is a close linkage among social networks, transportation networks, disease spread, and early detection. Spatial dimensions related to public gathering places such as hospitals, nursing homes, and restaurants, can play a major role in epidemics [Klovdahl et. al. 2001]. Like natural epidemics, bioterrorist attacks unfold within spatially defined, complex social systems, and the societal and networked response can have profound effects on their outcome. This paper focuses on bioterrorist attacks, but the model has been applied to emergent and familiar diseases as well.

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

Physical Complexity and Cognitive Evolution

NASA Astrophysics Data System (ADS)

Jedlicka, Peter

Our intuition tells us that there is a general trend in the evolution of nature, a trend towards greater complexity. However, there are several definitions of complexity and hence it is difficult to argue for or against the validity of this intuition. Christoph Adami has recently introduced a novel measure called physical complexity that assigns low complexity to both ordered and random systems and high complexity to those in between. Physical complexity measures the amount of information that an organism stores in its genome about the environment in which it evolves. The theory of physical complexity predicts that evolution increases the amount of `knowledge' an organism accumulates about its niche. It might be fruitful to generalize Adami's concept of complexity to the entire evolution (including the evolution of man). Physical complexity fits nicely into the philosophical framework of cognitive biology which considers biological evolution as a progressing process of accumulation of knowledge (as a gradual increase of epistemic complexity). According to this paradigm, evolution is a cognitive `ratchet' that pushes the organisms unidirectionally towards higher complexity. Dynamic environment continually creates problems to be solved. To survive in the environment means to solve the problem, and the solution is an embodied knowledge. Cognitive biology (as well as the theory of physical complexity) uses the concepts of information and entropy and views the evolution from both the information-theoretical and thermodynamical perspective. Concerning humans as conscious beings, it seems necessary to postulate an emergence of a new kind of knowledge - a self-aware and self-referential knowledge. Appearence of selfreflection in evolution indicates that the human brain reached a new qualitative level in the epistemic complexity.

Theoretical aspects of Systems Biology.

PubMed

Bizzarri, Mariano; Palombo, Alessandro; Cucina, Alessandra

2013-05-01

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Electronic health records (EHRs): supporting ASCO's vision of cancer care.

PubMed

Yu, Peter; Artz, David; Warner, Jeremy

2014-01-01

ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior. The Agency for Healthcare Research and Quality promotes CDSS based on clinical practice guidelines, which are knowledge bases that grow too slowly to match the rate of panomic-derived knowledge. CDSS that are based on systems biology models will be more easily adaptable to rapid advancements and translational medicine. We describe the characteristics of health data representation, a model for representing molecular data that supports data extraction and use for panomic-based clinical research, and argue for CDSS that are based on systems biology and are algorithm-based.

Graphics processing units in bioinformatics, computational biology and systems biology.

PubMed

Nobile, Marco S; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

2017-09-01

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools. © The Author 2016. Published by Oxford University Press.

Web-based applications for building, managing and analysing kinetic models of biological systems.

PubMed

Lee, Dong-Yup; Saha, Rajib; Yusufi, Faraaz Noor Khan; Park, Wonjun; Karimi, Iftekhar A

2009-01-01

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

Providing data science support for systems pharmacology and its implications to drug discovery.

PubMed

Hart, Thomas; Xie, Lei

2016-01-01

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

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.

Agent-Based Modeling in Molecular Systems Biology.

PubMed

Soheilypour, Mohammad; Mofrad, Mohammad R K

2018-07-01

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease. © 2018 WILEY Periodicals, Inc.

Genetic control of root growth: from genes to networks.

PubMed

Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B; Ristova, Daniela; Busch, Wolfgang

2016-01-01

Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Self-transcending meditation is good for mental health: why this should be the case.

PubMed

Hankey, Alex; Shetkar, Rashmi

2016-06-01

A simple theory of health has recently been proposed: while poor quality regulation corresponds to poor quality health so that improving regulation should improve health, optimal regulation optimizes function and optimizes health. Examining the term 'optimal regulation' in biological systems leads to a straightforward definition in terms of 'criticality' in complexity biology, a concept that seems to apply universally throughout biology. Criticality maximizes information processing and sensitivity of response to external stimuli, and for these reasons may be held to optimize regulation. In this way a definition of health has been given in terms of regulation, a scientific concept, which ties into detailed properties of complex systems, including brain cortices, and mental health. Models of experience and meditation built on complexity also point to criticality: it represents the condition making self-awareness possible, and is strengthened by meditation practices leading to the state of pure consciousness-the content-free state of mind in deep meditation. From this it follows that healthy function of the brain cortex, its sensitivity,y and consistency of response to external challenges should improve by practicing techniques leading to content-free awareness-transcending the original focus introduced during practice. Evidence for this is reviewed.

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data.

PubMed

Linard, Benjamin; Nguyen, Ngoc Hoan; Prosdocimi, Francisco; Poch, Olivier; Thompson, Julie D

2012-01-01

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.

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

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

PubMed Central

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

PubMed

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P

2014-01-01

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1). Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

The Unicellular State as a Point Source in a Quantum Biological System

PubMed Central

Torday, John S.; Miller, William B.

2016-01-01

A point source is the central and most important point or place for any group of cohering phenomena. Evolutionary development presumes that biological processes are sequentially linked, but neither directed from, nor centralized within, any specific biologic structure or stage. However, such an epigenomic entity exists and its transforming effects can be understood through the obligatory recapitulation of all eukaryotic lifeforms through a zygotic unicellular phase. This requisite biological conjunction can now be properly assessed as the focal point of reconciliation between biology and quantum phenomena, illustrated by deconvoluting complex physiologic traits back to their unicellular origins. PMID:27240413

A dedicated database system for handling multi-level data in systems biology.

PubMed

Pornputtapong, Natapol; Wanichthanarak, Kwanjeera; Nilsson, Avlant; Nookaew, Intawat; Nielsen, Jens

2014-01-01

Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

The Stress Response Systems: Universality and Adaptive Individual Differences

ERIC Educational Resources Information Center

Ellis, Bruce J.; Jackson, Jenee James; Boyce, W. Thomas

2006-01-01

Biological reactivity to psychological stressors comprises a complex, integrated system of central neural and peripheral neuroendocrine responses designed to prepare the organism for challenge or threat. Developmental experience plays a role, along with heritable variation, in calibrating the response dynamics of this system. This calibration…

Spontaneous switching of frequency-locking by periodic stimulus in oscillators of plasmodium of the true slime mold.

PubMed

Takamatsu, A; Yamamoto, T; Fujii, T

2004-01-01

Microfabrication technique was used to construct a model system with a living cell of plasmodium of the true slime mold, Physarum polycephalum, a living coupled oscillator system. Its parameters can be systematically controlled as in computer simulations, so that results are directly comparable to those of general mathematical models. As the first step, we investigated responses in oscillatory cells, the oscillators of the plasmodium, to periodic stimuli by temperature changes to elucidate characteristics of the cells as nonlinear systems whose internal dynamics are unknown because of their complexity. We observed that the forced oscillator of the plasmodium show 1:1, 2:1, 3:1 frequency locking inside so-called Arnold tongues regions as well as in other nonlinear systems such as chemical systems and other biological systems. In addition, we found spontaneous switching behavior from certain frequency locking states to other states, even under certain fixed parameters. This technique can be applied to more complex systems with multiple elements, such as coupled oscillator systems, and would be useful to investigate complicated phenomena in biological systems such as information processing.

20170312 - Computer Simulation of Developmental ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Computer Simulation of Developmental Processes and ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Towards a global initiative for fibrosis treatment (GIFT)

PubMed Central

Agusti, Alvar; Crestani, Bruno; Schwartz, David A.; Chambers, Rachel C.; Maher, Toby M.; Faner, Rosa; Mora, Ana Lucia; Rojas, Mauricio; Antoniou, Katerina M.; Sellares, Jacobo

2017-01-01

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterised by increased scarring of lung tissue. Despite the recent introduction of novel drugs that slow disease progression, IPF remains a deadly disease, and the benefits of these new drugs differ markedly between patients. Human diseases arise due to alterations in an almost limitless network of interconnected genes, proteins, metabolites, cells and tissues, in direct relationship with a continuously changing macro- or microenvironment. Systems biology is a novel research strategy that seeks to understand the structure and behaviour of the so-called “emergent properties” of complex systems, such as those involved in disease pathogenesis, which are most often overlooked when just one element of disease pathogenesis is observed in isolation. This article summarises the debate that took place during a European Respiratory Society research seminar in Barcelona, Spain on December 15–16, 2016, which focused on how systems biology could generate new data by integrating the different IPF pathogenic levels of complexity. The main conclusion of the seminar was to create a global initiative to improve IPF outcomes by integrating cutting-edge international research that leverages systems biology to develop a precision medicine approach to tackle this devastating disease. PMID:29214157

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Integrating biological redesign: where synthetic biology came from and where it needs to go.

PubMed

Way, Jeffrey C; Collins, James J; Keasling, Jay D; Silver, Pamela A

2014-03-27

Synthetic biology seeks to extend approaches from engineering and computation to redesign of biology, with goals such as generating new chemicals, improving human health, and addressing environmental issues. Early on, several guiding principles of synthetic biology were articulated, including design according to specification, separation of design from fabrication, use of standardized biological parts and organisms, and abstraction. We review the utility of these principles over the past decade in light of the field's accomplishments in building complex systems based on microbial transcription and metabolism and describe the progress in mammalian cell engineering. Copyright © 2014 Elsevier Inc. All rights reserved.

Environmental Modeling

EPA Pesticide Factsheets

EPA's modeling community is working to gain insights into certain parts of a physical, biological, economic, or social system by conducting environmental assessments for Agency decision making to complex environmental issues.

Network dynamics and systems biology

NASA Astrophysics Data System (ADS)

Norrell, Johannes A.

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

Complex systems: physics beyond physics

NASA Astrophysics Data System (ADS)

Holovatch, Yurij; Kenna, Ralph; Thurner, Stefan

2017-03-01

Complex systems are characterised by specific time-dependent interactions among their many constituents. As a consequence they often manifest rich, non-trivial and unexpected behaviour. Examples arise both in the physical and non-physical worlds. The study of complex systems forms a new interdisciplinary research area that cuts across physics, biology, ecology, economics, sociology, and the humanities. In this paper we review the essence of complex systems from a physicists' point of view, and try to clarify what makes them conceptually different from systems that are traditionally studied in physics. Our goal is to demonstrate how the dynamics of such systems may be conceptualised in quantitative and predictive terms by extending notions from statistical physics and how they can often be captured in a framework of co-evolving multiplex network structures. We mention three areas of complex-systems science that are currently studied extensively, the science of cities, dynamics of societies, and the representation of texts as evolutionary objects. We discuss why these areas form complex systems in the above sense. We argue that there exists plenty of new ground for physicists to explore and that methodical and conceptual progress is needed most.

Hybrid organic-inorganic system for producing biofuels

DOEpatents

Yeh, Yi-Chun; Singer, Steven W.; Chhabra, Swapnil R.; Beller, Harry R.; Mueller, Jana

2017-10-03

The present invention provides for a system for converting CO.sub.2 and H.sub.2 to one or more biologically derived compounds. In some embodiments, the system comprises a host cell comprising one or more nucleic acids encoding genes for a recombinant surface display protein which is capable of tethering an electrocatalyst molecule, such as a cobalt(II) complex supported by tetradentate polypyridyl ligand 2-bis(2-pyridyl)(methoxy)methyl-6-pyridylpyridine (PY4), and enzymes for synthesizing a biologically derived compound, such as an alkane, alcohol, fatty acid, ester, or isoprenoid.

Integration of functional myotubes with a Bio-MEMS device for non-invasive interrogation.

PubMed

Wilson, Kerry; Molnar, Peter; Hickman, James

2007-07-01

We have developed a biological micro-electromechanical system (Bio-MEMS) device consisting of surface-modified microfabricated silicon cantilevers and an AFM detection apparatus for the study of cultured myotubes. With this system we are able to selectively stimulate the myotubes as well as report on a variety of physiological properties of the myotubes in real time and in a high-throughput manner. This system will serve as the foundation for future work integrating multiple tissue types for the creation of Bio-MEMS analogues of complex tissues and biological circuits.

A CellML simulation compiler and code generator using ODE solving schemes

PubMed Central

2012-01-01

Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler. PMID:23083065

Near Infrared Dyes as Lifetime Solvatochromic Probes for Micropolarity Measurements of Biological Systems

PubMed Central

Berezin, Mikhail Y.; Lee, Hyeran; Akers, Walter; Achilefu, Samuel

2007-01-01

The polarity of biological mediums controls a host of physiological processes such as digestion, signaling, transportation, metabolism, and excretion. With the recent widespread use of near-infrared (NIR) fluorescent dyes for biological imaging of cells and living organisms, reporting medium polarity with these dyes would provide invaluable functional information in addition to conventional optical imaging parameters. Here, we report a new approach to determine polarities of macro- and microsystems for in vitro and potential in vivo applications using NIR polymethine molecular probes. Unlike the poor solvatochromic response of NIR dyes in solvents with diverse polarity, their fluorescence lifetimes are highly sensitive, increasing by a factor of up to 8 on moving from polar to nonpolar mediums. We also established a correlation between fluorescence lifetime and solvent orientation polarizability and developed a lifetime polarity index for determining the polarity of complex systems, including micelles and albumin binding sites. Because of the importance of medium polarity in molecular, cellular, and biochemical processes and the significance of reduced autofluorescence and deep tissue penetration of light in the NIR region, the findings reported herein represent an important advance toward using NIR molecular probes to measure the polarity of complex biological systems in vitro and in vivo. PMID:17573433

Insect-gene-activity detection system for chemical and biological warfare agents and toxic industrial chemicals

NASA Astrophysics Data System (ADS)

Mackie, Ryan S.; Schilling, Amanda S.; Lopez, Arturo M.; Rayms-Keller, Alfredo

2002-02-01

Detection of multiple chemical and biological weapons (CBW) agents and/or complex mixtures of toxic industrial chemicals (TIC) is imperative for both the commercial and military sectors. In a military scenario, a multi-CBW attack would create confusion, thereby delaying decontamination and therapeutic efforts. In the commercial sector, polluted sites invariably contain a mixture of TIC. Novel detection systems capable of detecting CBW and TIC are sorely needed. While it may be impossible to build a detector capable of discriminating all the possible combinations of CBW, a detection system capable of statistically predicting the most likely composition of a given mixture is within the reach of current emerging technologies. Aquatic insect-gene activity may prove to be a sensitive, discriminating, and elegant paradigm for the detection of CBW and TIC. We propose to systematically establish the expression patterns of selected protein markers in insects exposed to specific mixtures of chemical and biological warfare agents to generate a library of biosignatures of exposure. The predicting capabilities of an operational library of biosignatures of exposures will allow the detection of emerging novel or genetically engineered agents, as well as complex mixtures of chemical and biological weapons agents. CBW and TIC are discussed in the context of war, terrorism, and pollution.

On a biologically inspired topology optimization method

NASA Astrophysics Data System (ADS)

Kobayashi, Marcelo H.

2010-03-01

This work concerns the development of a biologically inspired methodology for the study of topology optimization in engineering and natural systems. The methodology is based on L systems and its turtle interpretation for the genotype-phenotype modeling of the topology development. The topology is analyzed using the finite element method, and optimized using an evolutionary algorithm with the genetic encoding of the L system and its turtle interpretation, as well as, body shape and physical characteristics. The test cases considered in this work clearly show the suitability of the proposed method for the study of engineering and natural complex systems.

Estimating the dilemma strength for game systems. Comment on "Universal scaling for the dilemma strength in evolutionary games", by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Chen, Xiaojie

2015-09-01

The puzzle of cooperation exists widely in the realistic world, including biological, social, and engineering systems. How to solve the cooperation puzzle has received considerable attention in recent years [1]. Evolutionary game theory provides a common mathematical framework to study the problem of cooperation. In principle, these practical biological, social, or engineering systems can be described by complex game models composed of multiple autonomous individuals with mutual interactions. And generally there exists a dilemma for the evolution of cooperation in the game systems.

General and craniofacial development are complex adaptive processes influenced by diversity.

PubMed

Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C

2014-06-01

Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care. © 2014 Australian Dental Association.

Structure Prediction of Protein Complexes

NASA Astrophysics Data System (ADS)

Pierce, Brian; Weng, Zhiping

Protein-protein interactions are critical for biological function. They directly and indirectly influence the biological systems of which they are a part. Antibodies bind with antigens to detect and stop viruses and other infectious agents. Cell signaling is performed in many cases through the interactions between proteins. Many diseases involve protein-protein interactions on some level, including cancer and prion diseases.

The Use of Avian Focal Species for Conservation Planning in California

Treesearch

Mary K. Chase; Geoffrey R. Geupel

2005-01-01

Conservationists often try to facilitate the complex task of protecting biological diversity by choosing a subset of species from a larger community to help them plan their conservation objectives. Biological knowledge about these species then is used to plan reserve systems or to guide habitat restoration and management efforts, with the assumption that the...

Holonic Rationale and Bio-inspiration on Design of Complex Emergent and Evolvable Systems

NASA Astrophysics Data System (ADS)

Leitao, Paulo

Traditional centralized and rigid control structures are becoming inflexible to face the requirements of reconfigurability, responsiveness and robustness, imposed by customer demands in the current global economy. The Holonic Manufacturing Systems (HMS) paradigm, which was pointed out as a suitable solution to face these requirements, translates the concepts inherited from social organizations and biology to the manufacturing world. It offers an alternative way of designing adaptive systems where the traditional centralized control is replaced by decentralization over distributed and autonomous entities organized in hierarchical structures formed by intermediate stable forms. In spite of its enormous potential, methods regarding the self-adaptation and self-organization of complex systems are still missing. This paper discusses how the insights from biology in connection with new fields of computer science can be useful to enhance the holonic design aiming to achieve more self-adaptive and evolvable systems. Special attention is devoted to the discussion of emergent behavior and self-organization concepts, and the way they can be combined with the holonic rationale.

A Fast Algorithm for Massively Parallel, Long-Term, Simulation of Complex Molecular Dynamics Systems

NASA Technical Reports Server (NTRS)

Jaramillo-Botero, Andres; Goddard, William A, III; Fijany, Amir

1997-01-01

The advances in theory and computing technology over the last decade have led to enormous progress in applying atomistic molecular dynamics (MD) methods to the characterization, prediction, and design of chemical, biological, and material systems,.

Circadian systems biology in Metazoa.

PubMed

Lin, Li-Ling; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

2015-11-01

Systems biology, which can be defined as integrative biology, comprises multistage processes that can be used to understand components of complex biological systems of living organisms and provides hierarchical information to decoding life. Using systems biology approaches such as genomics, transcriptomics and proteomics, it is now possible to delineate more complicated interactions between circadian control systems and diseases. The circadian rhythm is a multiscale phenomenon existing within the body that influences numerous physiological activities such as changes in gene expression, protein turnover, metabolism and human behavior. In this review, we describe the relationships between the circadian control system and its related genes or proteins, and circadian rhythm disorders in systems biology studies. To maintain and modulate circadian oscillation, cells possess elaborative feedback loops composed of circadian core proteins that regulate the expression of other genes through their transcriptional activities. The disruption of these rhythms has been reported to be associated with diseases such as arrhythmia, obesity, insulin resistance, carcinogenesis and disruptions in natural oscillations in the control of cell growth. This review demonstrates that lifestyle is considered as a fundamental factor that modifies circadian rhythm, and the development of dysfunctions and diseases could be regulated by an underlying expression network with multiple circadian-associated signals. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Systems biology of personalized nutrition

PubMed Central

van Ommen, Ben; van den Broek, Tim; de Hoogh, Iris; van Erk, Marjan; van Someren, Eugene; Rouhani-Rankouhi, Tanja; Anthony, Joshua C; Hogenelst, Koen; Pasman, Wilrike; Boorsma, André; Wopereis, Suzan

2017-01-01

Abstract Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person’s health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology–based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of “systems flexibility” is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed. PMID:28969366

Growing trend of CE at the omics level: the frontier of systems biology.

PubMed

Oh, Eulsik; Hasan, Md Nabiul; Jamshed, Muhammad; Park, Soo Hyun; Hong, Hye-Min; Song, Eun Joo; Yoo, Young Sook

2010-01-01

In a novel attempt to comprehend the complexity of life, systems biology has recently emerged as a state-of-the-art approach for biological research in contrast to the reductionist approaches that have been used in molecular cell biology since the 1950s. Because a massive amount of information is required in many systems biology studies of life processes, we have increasingly come to depend on techniques that provide high-throughput omics data. CE and CE coupled to MS have served as powerful analytical tools for providing qualitative and quantitative omics data. Recent systems biology studies have focused strongly on the diagnosis and treatment of diseases. The increasing number of clinical research papers on drug discovery and disease therapies reflects this growing interest among scientists. Since such clinical research reflects one of the ultimate purposes of bioscience, these trends will be sustained for a long time. Thus, this review mainly focuses on the application of CE and CE-MS in diagnosis as well as on the latest CE methods developed. Furthermore, we outline the new challenges that arose in 2008 and later in elucidating the system-level functions of the bioconstituents of living organisms.

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

PubMed

O'Neill, M A; Hilgetag, C C

2001-08-29

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

PubMed Central

O'Neill, M A; Hilgetag, C C

2001-01-01

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement. PMID:11545702

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

NASA Astrophysics Data System (ADS)

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

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

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.

Complexes of the ATP-dependent Lon protease and DNA aptamers with G-quadruplexes as a model for developing a nanosensor biomagnetic immunoassay system

NASA Astrophysics Data System (ADS)

Spiridonova, V. A.; Sizov, V. A.; Kuzmenko, E. O.; Melnichuk, A. V.; Oleinichenko, E. A.; Kudzhaev, A. M.; Rotanova, T. V.; Snigirev, O. V.

2017-07-01

The binding to Lon protease through biotinylated aptamers whose structures contain G-quadruplex fragments with magnetic nanoparticles (MNPs) functionalized by streptavidin was investigated. The conditions of binding of target aptamers to MNPs are met. The resulting complexes are proposed for detection of Lon protease in different biological sources and for constructing a novel biomagnetic nanosensor immunoassay system.

Programmable chemical controllers made from DNA.

PubMed

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2013-10-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

Programmable chemical controllers made from DNA

NASA Astrophysics Data System (ADS)

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2013-10-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

Programmable chemical controllers made from DNA

PubMed Central

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2014-01-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language', and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents. PMID:24077029

Recombinant protein expression for structural biology in HEK 293F suspension cells: a novel and accessible approach.

PubMed

Portolano, Nicola; Watson, Peter J; Fairall, Louise; Millard, Christopher J; Milano, Charles P; Song, Yun; Cowley, Shaun M; Schwabe, John W R

2014-10-16

The expression and purification of large amounts of recombinant protein complexes is an essential requirement for structural biology studies. For over two decades, prokaryotic expression systems such as E. coli have dominated the scientific literature over costly and less efficient eukaryotic cell lines. Despite the clear advantage in terms of yields and costs of expressing recombinant proteins in bacteria, the absence of specific co-factors, chaperones and post-translational modifications may cause loss of function, mis-folding and can disrupt protein-protein interactions of certain eukaryotic multi-subunit complexes, surface receptors and secreted proteins. The use of mammalian cell expression systems can address these drawbacks since they provide a eukaryotic expression environment. However, low protein yields and high costs of such methods have until recently limited their use for structural biology. Here we describe a simple and accessible method for expressing and purifying milligram quantities of protein by performing transient transfections of suspension grown HEK (Human Embryonic Kidney) 293 F cells.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

A method to identify and analyze biological programs through automated reasoning

PubMed Central

Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen

2016-01-01

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. PMID:27668090

Mosaic, self-similarity logic, and biological attraction principles: three explanatory instruments in biology.

PubMed

Agnati, Luigi F; Baluska, Frantisek; Barlow, Peter W; Guidolin, Diego

2009-11-01

From a structural standpoint, living organisms are organized like a nest of Russian matryoshka dolls, in which structures are buried within one another. From a temporal point of view, this type of organization is the result of a history comprised of a set of time backcloths which have accompanied the passage of living matter from its origins up to the present day. The aim of the present paper is to indicate a possible course of this 'passage through time, and suggest how today's complexity has been reached by living organisms. This investigation will employ three conceptual tools, namely the Mosaic, Self-Similarity Logic, and the Biological Attraction principles. Self-Similarity Logic indicates the self-consistency by which elements of a living system interact, irrespective of the spatiotemporal level under consideration. The term Mosaic indicates how, from the same set of elements assembled according to different patterns, it is possible to arrive at completely different constructions: hence, each system becomes endowed with different emergent properties. The Biological Attraction principle states that there is an inherent drive for association and merging of compatible elements at all levels of biological complexity. By analogy with the gravitation law in physics, biological attraction is based on the evidence that each living organism creates an attractive field around itself. This field acts as a sphere of influence that actively attracts similar fields of other biological systems, thereby modifying salient features of the interacting organisms. Three specific organizational levels of living matter, namely the molecular, cellular, and supracellular levels, have been considered in order to analyse and illustrate the interpretative as well as the predictive roles of each of these three explanatory principles.

Interactome Networks and Human Disease

PubMed Central

Vidal, Marc; Cusick, Michael E.; Barabási, Albert-László

2011-01-01

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease. PMID:21414488

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

PubMed

Cankorur-Cetinkaya, Ayca; Dias, Joao M L; Kludas, Jana; Slater, Nigel K H; Rousu, Juho; Oliver, Stephen G; Dikicioglu, Duygu

2017-06-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

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.

Thermodynamics and the evolution of a city: a tale of how Chicago came to be, from biophysical and socio-economic perspectives

EPA Science Inventory

Cities are complex organized systems, similar to biological and ecological systems in the way that they are structured and function. These systems are subject to the laws of thermodynamics and the principles of Energy Systems Theory (EST). Like other systems, cities experience l...

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.

Improved delivery through biological membranes. XXXL: Solubilization and stabilization of an estradiol chemical delivery system by modified beta-cyclodextrins.

PubMed

Brewster, M E; Estes, K S; Loftsson, T; Perchalski, R; Derendorf, H; Mullersman, G; Bodor, N

1988-11-01

A dihydropyridine in equilibrium pyridinium salt chemical delivery system (CDS) for estradiol (E2CDS) was complexed with various modified beta-cyclodextrins including hydroxyethyl-beta-cyclodextrin (HECD), hydroxypropyl-beta-cyclodextrin (HPCD), and heptakis(2,6-di-O-methyl)-beta-cyclodextrin (DMCD). Complex formation with all of these cyclodextrins resulted in dramatic increases in the water solubility of E2CDS. Studies on the complex of E2CDS and HPCD (E2CDS-CD) indicated that the encapsulated estrogen was approximately four times more stable than the unmanipulated CDS, producing an estimated half-life of degradation of 4 years compared with 1.2 years for the uncomplexed drug at room temperature. The complexation of E2CDS and HPCD also stabilized the dihydronicotinate in solutions containing potassium ferricyanide. This formulation was shown to be equivalent to E2CDS in dimethyl sulfoxide in delivering the oxidized, estradiol precursor (E2Q+) to the brain, and also produced similar biological responses; these included decreased luteinizing hormone (LH) secretion and a decrease in the rate of weight gain in castrated female rats.

Introduction to biological complexity as a missing link in drug discovery.

PubMed

Gintant, Gary A; George, Christopher H

2018-06-06

Despite a burgeoning knowledge of the intricacies and mechanisms responsible for human disease, technological advances in medicinal chemistry, and more efficient assays used for drug screening, it remains difficult to discover novel and effective pharmacologic therapies. Areas covered: By reference to the primary literature and concepts emerging from academic and industrial drug screening landscapes, the authors propose that this disconnect arises from the inability to scale and integrate responses from simpler model systems to outcomes from more complex and human-based biological systems. Expert opinion: Further collaborative efforts combining target-based and phenotypic-based screening along with systems-based pharmacology and informatics will be necessary to harness the technological breakthroughs of today to derive the novel drug candidates of tomorrow. New questions must be asked of enabling technologies-while recognizing inherent limitations-in a way that moves drug development forward. Attempts to integrate mechanistic and observational information acquired across multiple scales frequently expose the gap between our knowledge and our understanding as the level of complexity increases. We hope that the thoughts and actionable items highlighted will help to inform the directed evolution of the drug discovery process.

Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology.

PubMed

Koutinas, Michalis; Kiparissides, Alexandros; Pistikopoulos, Efstratios N; Mantalaris, Athanasios

2012-01-01

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals.

Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology

PubMed Central

Koutinas, Michalis; Kiparissides, Alexandros; Pistikopoulos, Efstratios N.; Mantalaris, Athanasios

2013-01-01

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals. PMID:24688682

A systems biology analysis of autophagy in cancer therapy.

PubMed

Shi, Zheng; Li, Chun-yang; Zhao, Si; Yu, Yang; An, Na; Liu, Yong-xi; Wu, Chuan-fang; Yue, Bi-song; Bao, Jin-ku

2013-09-01

Autophagy, which degrades redundant or damaged cellular constituents, is intricately relevant to a variety of human diseases, most notably cancer. Autophagy exerts distinct effects on cancer initiation and progression, due to the intrinsic overlapping of autophagic and cancer signalling pathways. However, due to the complexity of cancer as a systemic disease, the fate of cancer cells is not decided by any one signalling pathway. Numerous autophagic inter-connectivity and cross-talk pathways need to be further clarified at a systems level. In this review, we propose a systems biology perspective for the comprehensive analysis of the autophagy-cancer network, focusing on systems biology analysis in autophagy and cancer therapy. Together, these analyses may not only improve our understanding on autophagy-cancer relationships, but also facilitate cancer drug discovery. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Complex Physical, Biophysical and Econophysical Systems

NASA Astrophysics Data System (ADS)

Dewar, Robert L.; Detering, Frank

1. Introduction to complex and econophysics systems: a navigation map / T. Aste and T. Di Matteo -- 2. An introduction to fractional diffusion / B. I. Henry, T.A.M. Langlands and P. Straka -- 3. Space plasmas and fusion plasmas as complex systems / R. O. Dendy -- 4. Bayesian data analysis / M. S. Wheatland -- 5. Inverse problems and complexity in earth system science / I. G. Enting -- 6. Applied fluid chaos: designing advection with periodically reoriented flows for micro to geophysical mixing and transport enhancement / G. Metcalfe -- 7. Approaches to modelling the dynamical activity of brain function based on the electroencephalogram / D. T. J. Liley and F. Frascoli -- 8. Jaynes' maximum entropy principle, Riemannian metrics and generalised least action bound / R. K. Niven and B. Andresen -- 9. Complexity, post-genomic biology and gene expression programs / R. B. H. Williams and O. J.-H. Luo -- 10. Tutorials on agent-based modelling with NetLogo and network analysis with Pajek / M. J. Berryman and S. D. Angus.

Decoding the complex genetic causes of heart diseases using systems biology.

PubMed

Djordjevic, Djordje; Deshpande, Vinita; Szczesnik, Tomasz; Yang, Andrian; Humphreys, David T; Giannoulatou, Eleni; Ho, Joshua W K

2015-03-01

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

Complex between triple helix of collagen and double helix of DNA in aqueous solution.

PubMed

Mrevlishvili, George M; Svintradze, David V

2005-06-01

We demonstrate in this paper that one example of a biologically important and molecular self-assembling complex system is a collagen-DNA ordered aggregate which spontaneously forms in aqueous solutions. Interaction between the collagen and the DNA leads to destruction of the hydration shell of the triple helix and stabilization of the double helix structure. From a molecular biology point of view this nano-scale self-assembling superstructure could increase the stability of DNA against the nucleases during collagen diseases and the growth of collagen fibrills in the presence of DNA.

Elaboration and use of nickel planar macrocyclic complex-based sensors for the direct electrochemical measurement of nitric oxide in biological media.

PubMed

Bedioui, F; Trevin, S; Devynck, J; Lantoine, F; Brunet, A; Devynck, M A

1997-01-01

We describe here the electrochemical detection of nitric oxide, NO, in biological systems by using chemically modified ultramicro carbon electrodes. In the first part of the paper, the different steps involved in the electrochemical preparation and characterization of the nickel-based sensor are described. This is illustrated by the use of nickel(II) tetrasulfonated phthalocyanine complex. The second part of the paper describes two examples of the direct electrochemical measurement of NO production in human blood platelets and endothelial cells from umbilical cord vein.

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

PubMed

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

2014-01-15

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

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.

Fractals in biology and medicine

NASA Technical Reports Server (NTRS)

Havlin, S.; Buldyrev, S. V.; Goldberger, A. L.; Mantegna, R. N.; Ossadnik, S. M.; Peng, C. K.; Simons, M.; Stanley, H. E.

1995-01-01

Our purpose is to describe some recent progress in applying fractal concepts to systems of relevance to biology and medicine. We review several biological systems characterized by fractal geometry, with a particular focus on the long-range power-law correlations found recently in DNA sequences containing noncoding material. Furthermore, we discuss the finding that the exponent alpha quantifying these long-range correlations ("fractal complexity") is smaller for coding than for noncoding sequences. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the normal heart is characterized by long-range "anticorrelations" which are absent in the diseased heart.

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