1, 2, 3, 4: infusing quantitative literacy into introductory biology.

PubMed

Speth, Elena Bray; Momsen, Jennifer L; Moyerbrailean, Gregory A; Ebert-May, Diane; Long, Tammy M; Wyse, Sara; Linton, Debra

2010-01-01

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills.

1, 2, 3, 4: Infusing Quantitative Literacy into Introductory Biology

PubMed Central

Momsen, Jennifer L.; Moyerbrailean, Gregory A.; Ebert-May, Diane; Long, Tammy M.; Wyse, Sara; Linton, Debra

2010-01-01

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills. PMID:20810965

Integrating Quantitative Thinking into an Introductory Biology Course Improves Students' Mathematical Reasoning in Biological Contexts

ERIC Educational Resources Information Center

Hester, Susan; Buxner, Sanlyn; Elfring, Lisa; Nagy, Lisa

2014-01-01

Recent calls for improving undergraduate biology education have emphasized the importance of students learning to apply quantitative skills to biological problems. Motivated by students' apparent inability to transfer their existing quantitative skills to biological contexts, we designed and taught an introductory molecular and cell biology course…

Fostering synergy between cell biology and systems biology.

PubMed

Eddy, James A; Funk, Cory C; Price, Nathan D

2015-08-01

In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding. In turn, incorporating domain expertise and experimental data is crucial for building towards whole cell models. An iterative cycle of interaction between cell and systems biologists advances the goals of both fields and establishes a framework for mechanistic understanding of the genome-to-phenome relationship. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

On the Edge of Mathematics and Biology Integration: Improving Quantitative Skills in Undergraduate Biology Education

ERIC Educational Resources Information Center

Feser, Jason; Vasaly, Helen; Herrera, Jose

2013-01-01

In this paper, the authors describe how two institutions are helping their undergraduate biology students build quantitative competencies. Incorporation of quantitative skills and reasoning in biology are framed through a discussion of two cases that both concern introductory biology courses, but differ in the complexity of the mathematics and the…

Infusion of Quantitative and Statistical Concepts into Biology Courses Does Not Improve Quantitative Literacy

ERIC Educational Resources Information Center

Beck, Christopher W.

2018-01-01

Multiple national reports have pushed for the integration of quantitative concepts into the context of disciplinary science courses. The aim of this study was to evaluate the quantitative and statistical literacy of biology students and explore learning gains when those skills were taught implicitly in the context of biology. I examined gains in…

Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper we demonstrate how Morven, a computational framework which can perform qualitative, semi-quantitative, and quantitative simulation of dynamical systems using the same model formalism, is applied to study the osmotic stress response pathway in yeast. First the Morven framework itself is briefly introduced in terms of the model formalism employed and output format. We then built a qualitative model for the biophysical process of the osmoregulation in yeast, and a global qualitative-level picture was obtained through qualitative simulation of this model. Furthermore, we constructed a Morven model based on existing quantitative model of the osmoregulation system. This model was then simulated qualitatively, semi-quantitatively, and quantitatively. The obtained simulation results are presented with an analysis. Finally the future development of the Morven framework for modelling the dynamic biological systems is discussed. PMID:25864377

Integrating quantitative thinking into an introductory biology course improves students' mathematical reasoning in biological contexts.

PubMed

Hester, Susan; Buxner, Sanlyn; Elfring, Lisa; Nagy, Lisa

2014-01-01

Recent calls for improving undergraduate biology education have emphasized the importance of students learning to apply quantitative skills to biological problems. Motivated by students' apparent inability to transfer their existing quantitative skills to biological contexts, we designed and taught an introductory molecular and cell biology course in which we integrated application of prerequisite mathematical skills with biology content and reasoning throughout all aspects of the course. In this paper, we describe the principles of our course design and present illustrative examples of course materials integrating mathematics and biology. We also designed an outcome assessment made up of items testing students' understanding of biology concepts and their ability to apply mathematical skills in biological contexts and administered it as a pre/postcourse test to students in the experimental section and other sections of the same course. Precourse results confirmed students' inability to spontaneously transfer their prerequisite mathematics skills to biological problems. Pre/postcourse outcome assessment comparisons showed that, compared with students in other sections, students in the experimental section made greater gains on integrated math/biology items. They also made comparable gains on biology items, indicating that integrating quantitative skills into an introductory biology course does not have a deleterious effect on students' biology learning.

Integrating Quantitative Thinking into an Introductory Biology Course Improves Students’ Mathematical Reasoning in Biological Contexts

PubMed Central

Hester, Susan; Buxner, Sanlyn; Elfring, Lisa; Nagy, Lisa

2014-01-01

Recent calls for improving undergraduate biology education have emphasized the importance of students learning to apply quantitative skills to biological problems. Motivated by students’ apparent inability to transfer their existing quantitative skills to biological contexts, we designed and taught an introductory molecular and cell biology course in which we integrated application of prerequisite mathematical skills with biology content and reasoning throughout all aspects of the course. In this paper, we describe the principles of our course design and present illustrative examples of course materials integrating mathematics and biology. We also designed an outcome assessment made up of items testing students’ understanding of biology concepts and their ability to apply mathematical skills in biological contexts and administered it as a pre/postcourse test to students in the experimental section and other sections of the same course. Precourse results confirmed students’ inability to spontaneously transfer their prerequisite mathematics skills to biological problems. Pre/postcourse outcome assessment comparisons showed that, compared with students in other sections, students in the experimental section made greater gains on integrated math/biology items. They also made comparable gains on biology items, indicating that integrating quantitative skills into an introductory biology course does not have a deleterious effect on students’ biology learning. PMID:24591504

Focus issue: series on computational and systems biology.

PubMed

Gough, Nancy R

2011-09-06

The application of computational biology and systems biology is yielding quantitative insight into cellular regulatory phenomena. For the month of September, Science Signaling highlights research featuring computational approaches to understanding cell signaling and investigation of signaling networks, a series of Teaching Resources from a course in systems biology, and various other articles and resources relevant to the application of computational biology and systems biology to the study of signal transduction.

Systems cell biology

PubMed Central

Mast, Fred D.; Ratushny, Alexander V.

2014-01-01

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

Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast.

PubMed

Pang, Wei; Coghill, George M

2015-05-01

In this paper we demonstrate how Morven, a computational framework which can perform qualitative, semi-quantitative, and quantitative simulation of dynamical systems using the same model formalism, is applied to study the osmotic stress response pathway in yeast. First the Morven framework itself is briefly introduced in terms of the model formalism employed and output format. We then built a qualitative model for the biophysical process of the osmoregulation in yeast, and a global qualitative-level picture was obtained through qualitative simulation of this model. Furthermore, we constructed a Morven model based on existing quantitative model of the osmoregulation system. This model was then simulated qualitatively, semi-quantitatively, and quantitatively. The obtained simulation results are presented with an analysis. Finally the future development of the Morven framework for modelling the dynamic biological systems is discussed. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE)

PubMed Central

Stanhope, Liz; Ziegler, Laura; Haque, Tabassum; Le, Laura; Vinces, Marcelo; Davis, Gregory K.; Zieffler, Andrew; Brodfuehrer, Peter; Preest, Marion; M. Belitsky, Jason; Umbanhowar, Charles; Overvoorde, Paul J.

2017-01-01

Multiple reports highlight the increasingly quantitative nature of biological research and the need to innovate means to ensure that students acquire quantitative skills. We present a tool to support such innovation. The Biological Science Quantitative Reasoning Exam (BioSQuaRE) is an assessment instrument designed to measure the quantitative skills of undergraduate students within a biological context. The instrument was developed by an interdisciplinary team of educators and aligns with skills included in national reports such as BIO2010, Scientific Foundations for Future Physicians, and Vision and Change. Undergraduate biology educators also confirmed the importance of items included in the instrument. The current version of the BioSQuaRE was developed through an iterative process using data from students at 12 postsecondary institutions. A psychometric analysis of these data provides multiple lines of evidence for the validity of inferences made using the instrument. Our results suggest that the BioSQuaRE will prove useful to faculty and departments interested in helping students acquire the quantitative competencies they need to successfully pursue biology, and useful to biology students by communicating the importance of quantitative skills. We invite educators to use the BioSQuaRE at their own institutions. PMID:29196427

Systems cell biology.

PubMed

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

2014-09-15

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

Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE)

ERIC Educational Resources Information Center

Stanhope, Liz; Ziegler, Laura; Haque, Tabassum; Le, Laura; Vinces, Marcelo; Davis, Gregory K.; Zieffler, Andrew; Brodfuehrer, Peter; Preest, Marion; Belitsky, Jason M.; Umbanhowar, Charles, Jr.; Overvoorde, Paul J.

2017-01-01

Multiple reports highlight the increasingly quantitative nature of biological research and the need to innovate means to ensure that students acquire quantitative skills. We present a tool to support such innovation. The Biological Science Quantitative Reasoning Exam (BioSQuaRE) is an assessment instrument designed to measure the quantitative…

Using Active Learning to Teach Concepts and Methods in Quantitative Biology.

PubMed

Waldrop, Lindsay D; Adolph, Stephen C; Diniz Behn, Cecilia G; Braley, Emily; Drew, Joshua A; Full, Robert J; Gross, Louis J; Jungck, John A; Kohler, Brynja; Prairie, Jennifer C; Shtylla, Blerta; Miller, Laura A

2015-11-01

This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms. © The Author 2015. 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.

Dynamics of biological systems: role of systems biology in medical research.

PubMed

Assmus, Heike E; Herwig, Ralf; Cho, Kwang-Hyun; Wolkenhauer, Olaf

2006-11-01

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

Online interactive teaching modules enhance quantitative proficiency of introductory biology students.

PubMed

Thompson, Katerina V; Nelson, Kären C; Marbach-Ad, Gili; Keller, Michael; Fagan, William F

2010-01-01

There is widespread agreement within the scientific and education communities that undergraduate biology curricula fall short in providing students with the quantitative and interdisciplinary problem-solving skills they need to obtain a deep understanding of biological phenomena and be prepared fully to contribute to future scientific inquiry. MathBench Biology Modules were designed to address these needs through a series of interactive, Web-based modules that can be used to supplement existing course content across the biological sciences curriculum. The effect of the modules was assessed in an introductory biology course at the University of Maryland. Over the course of the semester, students showed significant increases in quantitative skills that were independent of previous math course work. Students also showed increased comfort with solving quantitative problems, whether or not they ultimately arrived at the correct answer. A survey of spring 2009 graduates indicated that those who had experienced MathBench in their course work had a greater appreciation for the role of mathematics in modern biology than those who had not used MathBench. MathBench modules allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses.

An engineering design approach to systems biology.

PubMed

Janes, Kevin A; Chandran, Preethi L; Ford, Roseanne M; Lazzara, Matthew J; Papin, Jason A; Peirce, Shayn M; Saucerman, Jeffrey J; Lauffenburger, Douglas A

2017-07-17

Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.

Online Interactive Teaching Modules Enhance Quantitative Proficiency of Introductory Biology Students

PubMed Central

Nelson, Kären C.; Marbach-Ad, Gili; Keller, Michael; Fagan, William F.

2010-01-01

There is widespread agreement within the scientific and education communities that undergraduate biology curricula fall short in providing students with the quantitative and interdisciplinary problem-solving skills they need to obtain a deep understanding of biological phenomena and be prepared fully to contribute to future scientific inquiry. MathBench Biology Modules were designed to address these needs through a series of interactive, Web-based modules that can be used to supplement existing course content across the biological sciences curriculum. The effect of the modules was assessed in an introductory biology course at the University of Maryland. Over the course of the semester, students showed significant increases in quantitative skills that were independent of previous math course work. Students also showed increased comfort with solving quantitative problems, whether or not they ultimately arrived at the correct answer. A survey of spring 2009 graduates indicated that those who had experienced MathBench in their course work had a greater appreciation for the role of mathematics in modern biology than those who had not used MathBench. MathBench modules allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses. PMID:20810959

Biphasic dose responses in biology, toxicology and medicine: accounting for their generalizability and quantitative features.

PubMed

Calabrese, Edward J

2013-11-01

The most common quantitative feature of the hormetic-biphasic dose response is its modest stimulatory response which at maximum is only 30-60% greater than control values, an observation that is consistently independent of biological model, level of organization (i.e., cell, organ or individual), endpoint measured, chemical/physical agent studied, or mechanism. This quantitative feature suggests an underlying "upstream" mechanism common across biological systems, therefore basic and general. Hormetic dose response relationships represent an estimate of the peak performance of integrative biological processes that are allometrically based. Hormetic responses reflect both direct stimulatory or overcompensation responses to damage induced by relatively low doses of chemical or physical agents. The integration of the hormetic dose response within an allometric framework provides, for the first time, an explanation for both the generality and the quantitative features of the hormetic dose response. Copyright © 2013 Elsevier Ltd. All rights reserved.

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

PubMed

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

Graphic Representation of Carbon Dioxide Equilibria in Biological Systems.

ERIC Educational Resources Information Center

Kindig, Neal B.; Filley, Giles F.

1983-01-01

The log C-pH diagram is a useful means of displaying quantitatively the many variables (including temperature) that determine acid-base equilibria in biological systems. Presents the diagram as extended to open/closed biological systems and derives a new water-ion balance method for determining equilibrium pH. (JN)

Simultaneous Quantitation of Atenolol, Metoprolol, and Propranolol in Biological Matrices Via LC/MS

DTIC Science & Technology

2005-05-01

Simultaneous Quantitation of Atenolol, Metoprolol , and Propranolol in Biological Matrices Via LC/MS Robert D. Johnson Russell J. Lewis Civil...authorized 1 SIMULTANEOUS QUANTITATION OF ATENOLOL, METOPROLOL , AND PROPRANOLOL IN BIOLOGICAL MATRICES VIA LC/MS INTRODUCTION The Federal Aviation...detect beta-blocker compounds such as atenolol, metoprolol , or propranolol in the submitted biological samples. In forensic toxicol- ogy laboratories

Toward Integration: From Quantitative Biology to Mathbio-Biomath?

PubMed Central

de Pillis, Lisette; Findley, Ann; Joplin, Karl; Pelesko, John; Nelson, Karen; Thompson, Katerina; Usher, David; Watkins, Joseph

2010-01-01

In response to the call of BIO2010 for integrating quantitative skills into undergraduate biology education, 30 Howard Hughes Medical Institute (HHMI) Program Directors at the 2006 HHMI Program Directors Meeting established a consortium to investigate, implement, develop, and disseminate best practices resulting from the integration of math and biology. With the assistance of an HHMI-funded mini-grant, led by Karl Joplin of East Tennessee State University, and support in institutional HHMI grants at Emory and University of Delaware, these institutions held a series of summer institutes and workshops to document progress toward and address the challenges of implementing a more quantitative approach to undergraduate biology education. This report summarizes the results of the four summer institutes (2007–2010). The group developed four draft white papers, a wiki site, and a listserv. One major outcome of these meetings is this issue of CBE—Life Sciences Education, which resulted from proposals at our 2008 meeting and a January 2009 planning session. Many of the papers in this issue emerged from or were influenced by these meetings. PMID:20810946

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

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

2014-01-01

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

Flexible automated approach for quantitative liquid handling of complex biological samples.

PubMed

Palandra, Joe; Weller, David; Hudson, Gary; Li, Jeff; Osgood, Sarah; Hudson, Emily; Zhong, Min; Buchholz, Lisa; Cohen, Lucinda H

2007-11-01

A fully automated protein precipitation technique for biological sample preparation has been developed for the quantitation of drugs in various biological matrixes. All liquid handling during sample preparation was automated using a Hamilton MicroLab Star Robotic workstation, which included the preparation of standards and controls from a Watson laboratory information management system generated work list, shaking of 96-well plates, and vacuum application. Processing time is less than 30 s per sample or approximately 45 min per 96-well plate, which is then immediately ready for injection onto an LC-MS/MS system. An overview of the process workflow is discussed, including the software development. Validation data are also provided, including specific liquid class data as well as comparative data of automated vs manual preparation using both quality controls and actual sample data. The efficiencies gained from this automated approach are described.

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.

Development and Assessment of Modules to Integrate Quantitative Skills in Introductory Biology Courses

PubMed Central

Hoffman, Kathleen; Leupen, Sarah; Dowell, Kathy; Kephart, Kerrie; Leips, Jeff

2016-01-01

Redesigning undergraduate biology courses to integrate quantitative reasoning and skill development is critical to prepare students for careers in modern medicine and scientific research. In this paper, we report on the development, implementation, and assessment of stand-alone modules that integrate quantitative reasoning into introductory biology courses. Modules are designed to improve skills in quantitative numeracy, interpreting data sets using visual tools, and making inferences about biological phenomena using mathematical/statistical models. We also examine demographic/background data that predict student improvement in these skills through exposure to these modules. We carried out pre/postassessment tests across four semesters and used student interviews in one semester to examine how students at different levels approached quantitative problems. We found that students improved in all skills in most semesters, although there was variation in the degree of improvement among skills from semester to semester. One demographic variable, transfer status, stood out as a major predictor of the degree to which students improved (transfer students achieved much lower gains every semester, despite the fact that pretest scores in each focus area were similar between transfer and nontransfer students). We propose that increased exposure to quantitative skill development in biology courses is effective at building competency in quantitative reasoning. PMID:27146161

Epigenetics meets mathematics: towards a quantitative understanding of chromatin biology.

PubMed

Steffen, Philipp A; Fonseca, João P; Ringrose, Leonie

2012-10-01

How fast? How strong? How many? So what? Why do numbers matter in biology? Chromatin binding proteins are forever in motion, exchanging rapidly between bound and free pools. How do regulatory systems whose components are in constant flux ensure stability and flexibility? This review explores the application of quantitative and mathematical approaches to mechanisms of epigenetic regulation. We discuss methods for measuring kinetic parameters and protein quantities in living cells, and explore the insights that have been gained by quantifying and modelling dynamics of chromatin binding proteins. Copyright © 2012 WILEY Periodicals, Inc.

Development and Assessment of Modules to Integrate Quantitative Skills in Introductory Biology Courses.

PubMed

Hoffman, Kathleen; Leupen, Sarah; Dowell, Kathy; Kephart, Kerrie; Leips, Jeff

2016-01-01

Redesigning undergraduate biology courses to integrate quantitative reasoning and skill development is critical to prepare students for careers in modern medicine and scientific research. In this paper, we report on the development, implementation, and assessment of stand-alone modules that integrate quantitative reasoning into introductory biology courses. Modules are designed to improve skills in quantitative numeracy, interpreting data sets using visual tools, and making inferences about biological phenomena using mathematical/statistical models. We also examine demographic/background data that predict student improvement in these skills through exposure to these modules. We carried out pre/postassessment tests across four semesters and used student interviews in one semester to examine how students at different levels approached quantitative problems. We found that students improved in all skills in most semesters, although there was variation in the degree of improvement among skills from semester to semester. One demographic variable, transfer status, stood out as a major predictor of the degree to which students improved (transfer students achieved much lower gains every semester, despite the fact that pretest scores in each focus area were similar between transfer and nontransfer students). We propose that increased exposure to quantitative skill development in biology courses is effective at building competency in quantitative reasoning. © 2016 K. Hoffman, S. Leupen, et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Infusing Quantitative Approaches throughout the Biological Sciences Curriculum

ERIC Educational Resources Information Center

Thompson, Katerina V.; Cooke, Todd J.; Fagan, William F.; Gulick, Denny; Levy, Doron; Nelson, Kären C.; Redish, Edward F.; Smith, Robert F.; Presson, Joelle

2013-01-01

A major curriculum redesign effort at the University of Maryland is infusing all levels of our undergraduate biological sciences curriculum with increased emphasis on interdisciplinary connections and quantitative approaches. The curriculum development efforts have largely been guided by recommendations in the National Research Council's "Bio…

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-04-01

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

Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform

ERIC Educational Resources Information Center

Emmert-Streib, Frank; Dehmer, Matthias; Lyardet, Fernando

2013-01-01

Within recent years, there is an increasing need to train students, from biology and beyond, in quantitative methods that are relevant to cope with data-driven biology. Systems Biology is such a field that places a particular focus on the functional aspect of biology and molecular interacting processes. This paper deals with the conceptual design…

A method for three-dimensional quantitative observation of the microstructure of biological samples

NASA Astrophysics Data System (ADS)

Wang, Pengfei; Chen, Dieyan; Ma, Wanyun; Wu, Hongxin; Ji, Liang; Sun, Jialin; Lv, Danyu; Zhang, Lu; Li, Ying; Tian, Ning; Zheng, Jinggao; Zhao, Fengying

2009-07-01

Contemporary biology has developed into the era of cell biology and molecular biology, and people try to study the mechanism of all kinds of biological phenomena at the microcosmic level now. Accurate description of the microstructure of biological samples is exigent need from many biomedical experiments. This paper introduces a method for 3-dimensional quantitative observation on the microstructure of vital biological samples based on two photon laser scanning microscopy (TPLSM). TPLSM is a novel kind of fluorescence microscopy, which has excellence in its low optical damage, high resolution, deep penetration depth and suitability for 3-dimensional (3D) imaging. Fluorescent stained samples were observed by TPLSM, and afterward the original shapes of them were obtained through 3D image reconstruction. The spatial distribution of all objects in samples as well as their volumes could be derived by image segmentation and mathematic calculation. Thus the 3-dimensionally and quantitatively depicted microstructure of the samples was finally derived. We applied this method to quantitative analysis of the spatial distribution of chromosomes in meiotic mouse oocytes at metaphase, and wonderful results came out last.

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

EPA Science Inventory

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

Toward Integration: From Quantitative Biology to Mathbio-Biomath?

ERIC Educational Resources Information Center

Marsteller, Pat; de Pillis, Lisette; Findley, Ann; Joplin, Karl; Pelesko, John; Nelson, Karen; Thompson, Katerina; Usher, David; Watkins, Joseph

2010-01-01

In response to the call of "BIO2010" for integrating quantitative skills into undergraduate biology education, 30 Howard Hughes Medical Institute (HHMI) Program Directors at the 2006 HHMI Program Directors Meeting established a consortium to investigate, implement, develop, and disseminate best practices resulting from the integration of math and…

Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy.

PubMed

Doblas, Ana; Sánchez-Ortiga, Emilio; Martínez-Corral, Manuel; Saavedra, Genaro; Garcia-Sucerquia, Jorge

2014-04-01

The advantages of using a telecentric imaging system in digital holographic microscopy (DHM) to study biological specimens are highlighted. To this end, the performances of nontelecentric DHM and telecentric DHM are evaluated from the quantitative phase imaging (QPI) point of view. The evaluated stability of the microscope allows single-shot QPI in DHM by using telecentric imaging systems. Quantitative phase maps of a section of the head of the drosophila melanogaster fly and of red blood cells are obtained via single-shot DHM with no numerical postprocessing. With these maps we show that the use of telecentric DHM provides larger field of view for a given magnification and permits more accurate QPI measurements with less number of computational operations.

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

PubMed

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

2018-01-01

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

A Transformative Model for Undergraduate Quantitative Biology Education

ERIC Educational Resources Information Center

Usher, David C.; Driscoll, Tobin A.; Dhurjati, Prasad; Pelesko, John A.; Rossi, Louis F.; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B.

2010-01-01

The "BIO2010" report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3)…

Quantitative evaluations of mechanisms of radiofrequency interactions with biological molecules and processes.

PubMed

Sheppard, Asher R; Swicord, Mays L; Balzano, Quirino

2008-10-01

The complexity of interactions of electromagnetic fields up to 10(12) Hz with the ions, atoms, and molecules of biological systems has given rise to a large number of established and proposed biophysical mechanisms applicable over a wide range of time and distance scales, field amplitudes, frequencies, and waveforms. This review focuses on the physical principles that guide quantitative assessment of mechanisms applicable for exposures at or below the level of endogenous electric fields associated with development, wound healing, and excitation of muscles and the nervous system (generally, 1 to 10(2) V m(-1)), with emphasis on conditions where temperature increases are insignificant (<1 K). Experiment and theory demonstrate possible demodulation at membrane barriers for frequencies < or =10 MHz, but not at higher frequencies. Although signal levels somewhat below system noise can be detected, signal-to-noise ratios substantially less than 0.1 cannot be overcome by cooperativity, signal averaging, coherent detection, or by nonlinear dynamical systems. Sensory systems and possible effects on biological magnetite suggest paradigms for extreme sensitivity at lower frequencies, but there are no known radiofrequency (RF) analogues. At the molecular level, vibrational modes are so overdamped by water molecules that excitation of molecular modes below the far infrared cannot occur. Two RF mechanisms plausibly may affect biological matter under common exposure conditions. For frequencies below approximately 150 MHz, shifts in the rate of chemical reactions can be mediated by radical pairs and, at all frequencies, dielectric and resistive heating can raise temperature and increase the entropy of the affected biological system.

Advancing metabolic engineering through systems biology of industrial microorganisms.

PubMed

Dai, Zongjie; Nielsen, Jens

2015-12-01

Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems.

PubMed

Kapoore, Rahul Vijay; Vaidyanathan, Seetharaman

2016-10-28

Metabolome analyses are a suite of analytical approaches that enable us to capture changes in the metabolome (small molecular weight components, typically less than 1500 Da) in biological systems. Mass spectrometry (MS) has been widely used for this purpose. The key challenge here is to be able to capture changes in a reproducible and reliant manner that is representative of the events that take place in vivo Typically, the analysis is carried out in vitro, by isolating the system and extracting the metabolome. MS-based approaches enable us to capture metabolomic changes with high sensitivity and resolution. When developing the technique for different biological systems, there are similarities in challenges and differences that are specific to the system under investigation. Here, we review some of the challenges in capturing quantitative changes in the metabolome with MS based approaches, primarily in microbial and mammalian systems.This article is part of the themed issue 'Quantitative mass spectrometry'. © 2016 The Author(s).

Quantitative measures of healthy aging and biological age

PubMed Central

Kim, Sangkyu; Jazwinski, S. Michal

2015-01-01

Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including ‘successful aging’ and ‘frailty’, have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the ‘frailty index’, which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity. PMID:26005669

Biological evolution of replicator systems: towards a quantitative approach.

PubMed

Martin, Osmel; Horvath, J E

2013-04-01

The aim of this work is to study the features of a simple replicator chemical model of the relation between kinetic stability and entropy production under the action of external perturbations. We quantitatively explore the different paths leading to evolution in a toy model where two independent replicators compete for the same substrate. To do that, the same scenario described originally by Pross (J Phys Org Chem 17:312-316, 2004) is revised and new criteria to define the kinetic stability are proposed. Our results suggest that fast replicator populations are continually favored by the effects of strong stochastic environmental fluctuations capable to determine the global population, the former assumed to be the only acting evolution force. We demonstrate that the process is continually driven by strong perturbations only, and that population crashes may be useful proxies for these catastrophic environmental fluctuations. As expected, such behavior is particularly enhanced under very large scale perturbations, suggesting a likely dynamical footprint in the recovery patterns of new species after mass extinction events in the Earth's geological past. Furthermore, the hypothesis that natural selection always favors the faster processes may give theoretical support to different studies that claim the applicability of maximum principles like the Maximum Metabolic Flux (MMF) or Maximum Entropy Productions Principle (MEPP), seen as the main goal of biological evolution.

Biological Evolution of Replicator Systems: Towards a Quantitative Approach

NASA Astrophysics Data System (ADS)

Martin, Osmel; Horvath, J. E.

2013-04-01

The aim of this work is to study the features of a simple replicator chemical model of the relation between kinetic stability and entropy production under the action of external perturbations. We quantitatively explore the different paths leading to evolution in a toy model where two independent replicators compete for the same substrate. To do that, the same scenario described originally by Pross (J Phys Org Chem 17:312-316, 2004) is revised and new criteria to define the kinetic stability are proposed. Our results suggest that fast replicator populations are continually favored by the effects of strong stochastic environmental fluctuations capable to determine the global population, the former assumed to be the only acting evolution force. We demonstrate that the process is continually driven by strong perturbations only, and that population crashes may be useful proxies for these catastrophic environmental fluctuations. As expected, such behavior is particularly enhanced under very large scale perturbations, suggesting a likely dynamical footprint in the recovery patterns of new species after mass extinction events in the Earth's geological past. Furthermore, the hypothesis that natural selection always favors the faster processes may give theoretical support to different studies that claim the applicability of maximum principles like the Maximum Metabolic Flux (MMF) or Maximum Entropy Productions Principle (MEPP), seen as the main goal of biological evolution.

Systems Biology in Immunology – A Computational Modeling Perspective

PubMed Central

Germain, Ronald N.; Meier-Schellersheim, Martin; Nita-Lazar, Aleksandra; Fraser, Iain D. C.

2011-01-01

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and conduct simulations of immune function, We provide descriptions of the key data gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease. PMID:21219182

Reshaping Plant Biology: Qualitative and Quantitative Descriptors for Plant Morphology

PubMed Central

Balduzzi, Mathilde; Binder, Brad M.; Bucksch, Alexander; Chang, Cynthia; Hong, Lilan; Iyer-Pascuzzi, Anjali S.; Pradal, Christophe; Sparks, Erin E.

2017-01-01

An emerging challenge in plant biology is to develop qualitative and quantitative measures to describe the appearance of plants through the integration of mathematics and biology. A major hurdle in developing these metrics is finding common terminology across fields. In this review, we define approaches for analyzing plant geometry, topology, and shape, and provide examples for how these terms have been and can be applied to plants. In leaf morphological quantifications both geometry and shape have been used to gain insight into leaf function and evolution. For the analysis of cell growth and expansion, we highlight the utility of geometric descriptors for understanding sepal and hypocotyl development. For branched structures, we describe how topology has been applied to quantify root system architecture to lend insight into root function. Lastly, we discuss the importance of using morphological descriptors in ecology to assess how communities interact, function, and respond within different environments. This review aims to provide a basic description of the mathematical principles underlying morphological quantifications. PMID:28217137

Dupuytren's: a systems biology disease

PubMed Central

2011-01-01

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule. PMID:21943049

Dual-wavelength common-path digital holographic microscopy for quantitative phase imaging of biological cells

NASA Astrophysics Data System (ADS)

Di, Jianglei; Song, Yu; Xi, Teli; Zhang, Jiwei; Li, Ying; Ma, Chaojie; Wang, Kaiqiang; Zhao, Jianlin

2017-11-01

Biological cells are usually transparent with a small refractive index gradient. Digital holographic interferometry can be used in the measurement of biological cells. We propose a dual-wavelength common-path digital holographic microscopy for the quantitative phase imaging of biological cells. In the proposed configuration, a parallel glass plate is inserted in the light path to create the lateral shearing, and two lasers with different wavelengths are used as the light source to form the dual-wavelength composite digital hologram. The information of biological cells for different wavelengths is separated and extracted in the Fourier domain of the hologram, and then combined to a shorter wavelength in the measurement process. This method could improve the system's temporal stability and reduce speckle noises simultaneously. Mouse osteoblastic cells and peony pollens are measured to show the feasibility of this method.

Prospects and challenges of quantitative phase imaging in tumor cell biology

NASA Astrophysics Data System (ADS)

Kemper, Björn; Götte, Martin; Greve, Burkhard; Ketelhut, Steffi

2016-03-01

Quantitative phase imaging (QPI) techniques provide high resolution label-free quantitative live cell imaging. Here, prospects and challenges of QPI in tumor cell biology are presented, using the example of digital holographic microscopy (DHM). It is shown that the evaluation of quantitative DHM phase images allows the retrieval of different parameter sets for quantification of cellular motion changes in migration and motility assays that are caused by genetic modifications. Furthermore, we demonstrate simultaneously label-free imaging of cell growth and morphology properties.

7th Annual Systems Biology Symposium: Systems Biology and Engineering

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

Galitski, Timothy P.

2008-04-01

Systems biology recognizes the complex multi-scale organization of biological systems, from molecules to ecosystems. The International Symposium on Systems Biology has been hosted by the Institute for Systems Biology in Seattle, Washington, since 2002. The annual two-day event gathers the most influential researchers transforming biology into an integrative discipline investingating complex systems. Engineering and application of new technology is a central element of systems biology. Genome-scale, or very small-scale, biological questions drive the enigneering of new technologies, which enable new modes of experimentation and computational analysis, leading to new biological insights and questions. Concepts and analytical methods in engineering aremore » now finding direct applications in biology. Therefore, the 2008 Symposium, funded in partnership with the Department of Energy, featured global leaders in "Systems Biology and Engineering."« less

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 characteristics of crucian by quantitative inspection method

NASA Astrophysics Data System (ADS)

Chu, Mengqi

2015-04-01

Biological characteristics of crucian by quantitative inspection method Through quantitative inspection method , the biological characteristics of crucian was preliminary researched. Crucian , Belongs to Cypriniformes, Cyprinidae, Carassius auratus, is a kind of main plant-eating omnivorous fish,like Gregarious, selection and ranking. Crucian are widely distributed, perennial water all over the country all have production. Determine the indicators of crucian in the experiment, to understand the growth, reproduction situation of crucian in this area . Using the measured data (such as the scale length ,scale size and wheel diameter and so on) and related functional to calculate growth of crucian in any one year.According to the egg shape, color, weight ,etc to determine its maturity, with the mean egg diameter per 20 eggs and the number of eggs per 0.5 grams, to calculate the relative and absolute fecundity of the fish .Measured crucian were female puberty. Based on the relation between the scale diameter and length and the information, linear relationship between crucian scale diameter and length: y=1.530+3.0649. From the data, the fertility and is closely relative to the increase of age. The older, the more mature gonad development. The more amount of eggs. In addition, absolute fecundity increases with the pituitary gland.Through quantitative check crucian bait food intake by the object, reveals the main food, secondary foods, and chance food of crucian ,and understand that crucian degree of be fond of of all kinds of bait organisms.Fish fertility with weight gain, it has the characteristics of species and populations, and at the same tmes influenced by the age of the individual, body length, body weight, environmental conditions (especially the nutrition conditions), and breeding habits, spawning times factors and the size of the egg. After a series of studies of crucian biological character, provide the ecological basis for local crucian's feeding, breeding

Sixth International Conference on Systems Biology (ICSB 2005)

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

Professor Andrew Murray

2005-10-22

This grant supported the Sixth International Conference on Systems Biology (ICSB 2005), held in Boston, Massachusetts from October 19th to 22nd, 2005. The ICSB is the only major, annual, international conference focused exclusively on the important emerging field of systems biology. It draws together scientists with expertise in theoretical, computational and experimental approaches to understanding biological systems at many levels. Previous ICSB meetings have been held in Tokyo (2000), at Caltech (2001), at the Karolinska Institute (2002), at Washington University in St. Louis (2003), and in Heidelberg (2004). These conferences have been increasingly successful at bringing together the growing communitymore » of established and junior researchers with interests in this area. Boston is home to several groups that have shown leadership in the field and was therefore an ideal place to hold this conference . The executive committee for the conference comprised Jim Collins (Biomedical Engineering, Boston University), Marc Kirschner (chair of the new Department of Systems Biology at Harvard Medical School), Eric Lander (director of the Broad Institute of MIT and Harvard), Andrew Murray (director of Harvard’s Bauer Center for Genomics Research) and Peter Sorger (director of MIT’s Computational and Systems Biology Initiative). There are almost as many definitions of systems biology as there are systems biologists. We take a broad view of the field, and we succeeded in one of our major aims in organizing a conference that bridges two types of divide. The first is that between traditional academic disciplines: each of our sessions includes speakers from biology and from one or more physical or quantitative sciences. The second type includes those that separate experimental biologists from their colleagues who work on theory or computation. Here again, each session included representatives from at least two of these three categories; indeed, many of the speakers

Improved Protein Arrays for Quantitative Systems Analysis of the Dynamics of Signaling Pathway Interactions

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

Yang, Chin-Rang

Astronauts and workers in nuclear plants who repeatedly exposed to low doses of ionizing radiation (IR, <10 cGy) are likely to incur specific changes in signal transduction and gene expression in various tissues of their body. Remarkable advances in high throughput genomics and proteomics technologies enable researchers to broaden their focus from examining single gene/protein kinetics to better understanding global gene/protein expression profiling and biological pathway analyses, namely Systems Biology. An ultimate goal of systems biology is to develop dynamic mathematical models of interacting biological systems capable of simulating living systems in a computer. This Glue Grant is to complementmore » Dr. Boothman’s existing DOE grant (No. DE-FG02-06ER64186) entitled “The IGF1/IGF-1R-MAPK-Secretory Clusterin (sCLU) Pathway: Mediator of a Low Dose IR-Inducible Bystander Effect” to develop sensitive and quantitative proteomic technology that suitable for low dose radiobiology researches. An improved version of quantitative protein array platform utilizing linear Quantum dot signaling for systematically measuring protein levels and phosphorylation states for systems biology modeling is presented. The signals are amplified by a confocal laser Quantum dot scanner resulting in ~1000-fold more sensitivity than traditional Western blots and show the good linearity that is impossible for the signals of HRP-amplification. Therefore this improved protein array technology is suitable to detect weak responses of low dose radiation. Software is developed to facilitate the quantitative readout of signaling network activities. Kinetics of EGFRvIII mutant signaling was analyzed to quantify cross-talks between EGFR and other signaling pathways.« less

Quantitative mass spectrometry of unconventional human biological matrices

NASA Astrophysics Data System (ADS)

Dutkiewicz, Ewelina P.; Urban, Pawel L.

2016-10-01

The development of sensitive and versatile mass spectrometric methodology has fuelled interest in the analysis of metabolites and drugs in unconventional biological specimens. Here, we discuss the analysis of eight human matrices-hair, nail, breath, saliva, tears, meibum, nasal mucus and skin excretions (including sweat)-by mass spectrometry (MS). The use of such specimens brings a number of advantages, the most important being non-invasive sampling, the limited risk of adulteration and the ability to obtain information that complements blood and urine tests. The most often studied matrices are hair, breath and saliva. This review primarily focuses on endogenous (e.g. potential biomarkers, hormones) and exogenous (e.g. drugs, environmental contaminants) small molecules. The majority of analytical methods used chromatographic separation prior to MS; however, such a hyphenated methodology greatly limits analytical throughput. On the other hand, the mass spectrometric methods that exclude chromatographic separation are fast but suffer from matrix interferences. To enable development of quantitative assays for unconventional matrices, it is desirable to standardize the protocols for the analysis of each specimen and create appropriate certified reference materials. Overcoming these challenges will make analysis of unconventional human biological matrices more common in a clinical setting. This article is part of the themed issue 'Quantitative mass spectrometry'.

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.

Development and Assessment of Modules to Integrate Quantitative Skills in Introductory Biology Courses

ERIC Educational Resources Information Center

Hoffman, Kathleen; Leupen, Sarah; Dowell, Kathy; Kephart, Kerrie; Leips, Jeff

2016-01-01

Redesigning undergraduate biology courses to integrate quantitative reasoning and skill development is critical to prepare students for careers in modern medicine and scientific research. In this paper, we report on the development, implementation, and assessment of stand-alone modules that integrate quantitative reasoning into introductory…

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

PubMed Central

Chen, Bor-Sen; Wu, Chia-Chou

2013-01-01

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

PubMed

Chen, Bor-Sen; Wu, Chia-Chou

2013-10-11

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Modeling and simulation of biological systems using SPICE language

PubMed Central

Lallement, Christophe; Haiech, Jacques

2017-01-01

The article deals with BB-SPICE (SPICE for Biochemical and Biological Systems), an extension of the famous Simulation Program with Integrated Circuit Emphasis (SPICE). BB-SPICE environment is composed of three modules: a new textual and compact description formalism for biological systems, a converter that handles this description and generates the SPICE netlist of the equivalent electronic circuit and NGSPICE which is an open-source SPICE simulator. In addition, the environment provides back and forth interfaces with SBML (System Biology Markup Language), a very common description language used in systems biology. BB-SPICE has been developed in order to bridge the gap between the simulation of biological systems on the one hand and electronics circuits on the other hand. Thus, it is suitable for applications at the interface between both domains, such as development of design tools for synthetic biology and for the virtual prototyping of biosensors and lab-on-chip. Simulation results obtained with BB-SPICE and COPASI (an open-source software used for the simulation of biochemical systems) have been compared on a benchmark of models commonly used in systems biology. Results are in accordance from a quantitative viewpoint but BB-SPICE outclasses COPASI by 1 to 3 orders of magnitude regarding the computation time. Moreover, as our software is based on NGSPICE, it could take profit of incoming updates such as the GPU implementation, of the coupling with powerful analysis and verification tools or of the integration in design automation tools (synthetic biology). PMID:28787027

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

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.

The use of continuous culture in systems biology investigations.

PubMed

Winder, Catherine L; Lanthaler, Karin

2011-01-01

When acquiring data for systems biology studies, it is essential to perform the experiments in controlled and reproducible conditions. Advances in the fields of proteomics and metabolomics allow the quantitative analysis of the components of the biological cell. It is essential to include a method in the experimental pipeline to culture the biological system in controlled and reproducible conditions to facilitate the acquisition of high-quality data. The employment of continuous culture methods for the growth of microorganisms is an ideal tool to achieve these objectives. This chapter will review the continuous culture approaches which may be applied in such studies, outline the experimental options which should be considered, and describe the approach applied in the production of steady-state cultures of Saccharomyces cerevisiae. Copyright © 2011 Elsevier Inc. All rights reserved.

Molecular biology of Homo sapiens: Abstracts of papers presented at the 51st Cold Spring Harbor symposium on quantitative biology

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

Watson, J.D.; Siniscalco, M.

This volume contains abstracts of papers presented at the 51st Cold Springs Harbor Symposium on Quantitative Biology. The topic for this meeting was the ''Molecular Biology of Homo sapiens.'' Sessions were entitled Human Gene Map, Human Cancer Genes, Genetic Diagnosis, Human Evolution, Drugs Made Off Human Genes, Receptors, and Gene Therapy. (DT)

An Ibm PC/AT-Based Image Acquisition And Processing System For Quantitative Image Analysis

NASA Astrophysics Data System (ADS)

Kim, Yongmin; Alexander, Thomas

1986-06-01

In recent years, a large number of applications have been developed for image processing systems in the area of biological imaging. We have already finished the development of a dedicated microcomputer-based image processing and analysis system for quantitative microscopy. The system's primary function has been to facilitate and ultimately automate quantitative image analysis tasks such as the measurement of cellular DNA contents. We have recognized from this development experience, and interaction with system users, biologists and technicians, that the increasingly widespread use of image processing systems, and the development and application of new techniques for utilizing the capabilities of such systems, would generate a need for some kind of inexpensive general purpose image acquisition and processing system specially tailored for the needs of the medical community. We are currently engaged in the development and testing of hardware and software for a fairly high-performance image processing computer system based on a popular personal computer. In this paper, we describe the design and development of this system. Biological image processing computer systems have now reached a level of hardware and software refinement where they could become convenient image analysis tools for biologists. The development of a general purpose image processing system for quantitative image analysis that is inexpensive, flexible, and easy-to-use represents a significant step towards making the microscopic digital image processing techniques more widely applicable not only in a research environment as a biologist's workstation, but also in clinical environments as a diagnostic tool.

Quantitation and detection of vanadium in biologic and pollution materials

NASA Technical Reports Server (NTRS)

Gordon, W. A.

1974-01-01

A review is presented of special considerations and methodology for determining vanadium in biological and air pollution materials. In addition to descriptions of specific analysis procedures, general sections are included on quantitation of analysis procedures, sample preparation, blanks, and methods of detection of vanadium. Most of the information presented is applicable to the determination of other trace elements in addition to vanadium.

Semi-automated 96-well liquid-liquid extraction for quantitation of drugs in biological fluids.

PubMed

Zhang, N; Hoffman, K L; Li, W; Rossi, D T

2000-02-01

A semi-automated liquid-liquid extraction (LLE) technique for biological fluid sample preparation was introduced for the quantitation of four drugs in rat plasma. All liquid transferring during the sample preparation was automated using a Tomtec Quadra 96 Model 320 liquid handling robot, which processed up to 96 samples in parallel. The samples were either in 96-deep-well plate or tube-rack format. One plate of samples can be prepared in approximately 1.5 h, and the 96-well plate is directly compatible with the autosampler of an LC/MS system. Selection of organic solvents and recoveries are discussed. Also, precision, relative error, linearity and quantitation of the semi automated LLE method are estimated for four example drugs using LC/MS/MS with a multiple reaction monitoring (MRM) approach. The applicability of this method and future directions are evaluated.

Longitudinal spatial coherence gated high-resolution tomography and quantitative phase microscopy of biological cells and tissues with uniform illumination

NASA Astrophysics Data System (ADS)

Mehta, Dalip Singh; Ahmad, Azeem; Dubey, Vishesh; Singh, Veena; Butola, Ankit; Mohanty, Tonmoy; Nandi, Sreyankar

2018-02-01

We report longitudinal spatial coherence (LSC) gated high-resolution tomography and quantitative phase microscopy of biological cells and tissues with uniform illumination using laser as a light source. To accomplish this a pseudo thermal light source was synthesized by passing laser beams through an optical system, which is basically a speckle reduction system with combined effect of spatial, temporal, angular and polarisation diversity. The longitudinal spatial coherence length of such light was significantly reduced by synthesizing a pseudo thermal source with the combined effect of spatial, angular and temporal diversity. This results in a low spatially coherent (i.e., broad angular frequency spectrum) light source with narrow temporal frequency spectrum. Light from such a pseudo thermal light source was passed through an interference microscope with varying magnification, such as, 10X and 50X. The interference microscope was used for full-field OCT imaging of multilayer objects and topography of industrial objects. Experimental results of optical sectioning of multilayer biological objects with high axial-resolution less than 10μm was achieved which is comparable to broadband white light source. The synthesized light source with reduced speckles having uniform illumination on the sample, which can be very useful for fluorescence microscopy as well as quantitative phase microscopy with less phase noise. The present system does not require any dispersion compensation optical system for biological samples as a highly monochromatic light source is used.

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.

Systems Biology, Neuroimaging, Neuropsychology, Neuroconnectivity and Traumatic Brain Injury

PubMed Central

Bigler, Erin D.

2016-01-01

The patient who sustains a traumatic brain injury (TBI) typically undergoes neuroimaging studies, usually in the form of computed tomography (CT) and magnetic resonance imaging (MRI). In most cases the neuroimaging findings are clinically assessed with descriptive statements that provide qualitative information about the presence/absence of visually identifiable abnormalities; though little if any of the potential information in a scan is analyzed in any quantitative manner, except in research settings. Fortunately, major advances have been made, especially during the last decade, in regards to image quantification techniques, especially those that involve automated image analysis methods. This review argues that a systems biology approach to understanding quantitative neuroimaging findings in TBI provides an appropriate framework for better utilizing the information derived from quantitative neuroimaging and its relation with neuropsychological outcome. Different image analysis methods are reviewed in an attempt to integrate quantitative neuroimaging methods with neuropsychological outcome measures and to illustrate how different neuroimaging techniques tap different aspects of TBI-related neuropathology. Likewise, how different neuropathologies may relate to neuropsychological outcome is explored by examining how damage influences brain connectivity and neural networks. Emphasis is placed on the dynamic changes that occur following TBI and how best to capture those pathologies via different neuroimaging methods. However, traditional clinical neuropsychological techniques are not well suited for interpretation based on contemporary and advanced neuroimaging methods and network analyses. Significant improvements need to be made in the cognitive and behavioral assessment of the brain injured individual to better interface with advances in neuroimaging-based network analyses. By viewing both neuroimaging and neuropsychological processes within a systems biology

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.

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

Explicit tracking of uncertainty increases the power of quantitative rule-of-thumb reasoning in cell biology.

PubMed

Johnston, Iain G; Rickett, Benjamin C; Jones, Nick S

2014-12-02

Back-of-the-envelope or rule-of-thumb calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behavior of physical systems, for example in so-called Fermi problems in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a probabilistic calculator called CALADIS, a free web tool, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between CALADIS and the BioNumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Computational systems chemical biology.

PubMed

Oprea, Tudor I; May, Elebeoba E; Leitão, Andrei; Tropsha, Alexander

2011-01-01

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Computational Systems Chemical Biology

PubMed Central

Oprea, Tudor I.; May, Elebeoba E.; Leitão, Andrei; Tropsha, Alexander

2013-01-01

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically-based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology, SCB (Oprea et al., 2007). The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology / systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology. PMID:20838980

Biological life-support systems

NASA Technical Reports Server (NTRS)

Shepelev, Y. Y.

1975-01-01

The establishment of human living environments by biologic methods, utilizing the appropriate functions of autotrophic and heterotrophic organisms is examined. Natural biologic systems discussed in terms of modeling biologic life support systems (BLSS), the structure of biologic life support systems, and the development of individual functional links in biologic life support systems are among the factors considered. Experimental modeling of BLSS in order to determine functional characteristics, mechanisms by which stability is maintained, and principles underlying control and regulation is also discussed.

Separating intrinsic from extrinsic fluctuations in dynamic biological systems

PubMed Central

Paulsson, Johan

2011-01-01

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

Separating intrinsic from extrinsic fluctuations in dynamic biological systems.

PubMed

Hilfinger, Andreas; Paulsson, Johan

2011-07-19

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

From systems biology to systems biomedicine.

PubMed

Antony, Paul M A; Balling, Rudi; Vlassis, Nikos

2012-08-01

Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine. Copyright © 2011 Elsevier Ltd. All rights reserved.

Systems Biology-an interdisciplinary approach.

PubMed

Friboulet, Alain; Thomas, Daniel

2005-06-15

System-level approaches in biology are not new but foundations of "Systems Biology" are achieved only now at the beginning of the 21st century [Kitano, H., 2001. Foundations of Systems Biology. MIT Press, Cambridge, MA]. The renewed interest for a system-level approach is linked to the progress in collecting experimental data and to the limits of the "reductionist" approach. System-level understanding of native biological and pathological systems is needed to provide potential therapeutic targets. Examples of interdisciplinary approach in Systems Biology are described in U.S., Japan and Europe. Robustness in biology, metabolic engineering and idiotypic networks are discussed in the framework of Systems Biology.

A survey of chemicals inducing lipid peroxidation in biological systems.

PubMed

Kappus, H

1987-01-01

A great number of drugs and chemicals are reviewed which have been shown to stimulate lipid peroxidation in any biological system. The underlying mechanisms, as far as known, are also dealt with. Lipid peroxidation induced by iron ions, organic hydroperoxides, halogenated hydrocarbons, redox cycling drugs, glutathione depleting chemicals, ethanol, heavy metals, ozone, nitrogen dioxide and a number of miscellaneous compounds, e.g. hydrazines, pesticides, antibiotics, are mentioned. It is shown that lipid peroxidation is stimulated by many of these compounds. However, quantitative estimates cannot be given yet and it is still impossible to judge the biological relevance of chemical-induced lipid peroxidation.

Frequency modulation atomic force microscopy: a dynamic measurement technique for biological systems

NASA Astrophysics Data System (ADS)

Higgins, Michael J.; Riener, Christian K.; Uchihashi, Takayuki; Sader, John E.; McKendry, Rachel; Jarvis, Suzanne P.

2005-03-01

Frequency modulation atomic force microscopy (FM-AFM) has been modified to operate in a liquid environment within an atomic force microscope specifically designed for investigating biological samples. We demonstrate the applicability of FM-AFM to biological samples using the spectroscopy mode to measure the unbinding forces of a single receptor-ligand (biotin-avidin) interaction. We show that quantitative adhesion force measurements can only be obtained provided certain modifications are made to the existing theory, which is used to convert the detected frequency shifts to an interaction force. Quantitative force measurements revealed that the unbinding forces for the biotin-avidin interaction were greater than those reported in previous studies. This finding was due to the use of high average tip velocities, which were calculated to be two orders of magnitude greater than those typically used in unbinding receptor-ligand experiments. This study therefore highlights the potential use of FM-AFM to study a range of biological systems, including living cells and/or single biomolecule interactions.

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

PubMed

Ban, Eunmi; Park, Soo Hyun; Kang, Min-Jung; Lee, Hyun-Jung; Song, Eun Joo; Yoo, Young Sook

2012-01-01

Omics is the study of proteins, peptides, genes, and metabolites in living organisms. Systems biology aims to understand the system through the study of the relationship between elements such as genes and proteins in biological system. Recently, systems biology emerged as the result of the advanced development of high-throughput analysis technologies such as DNA sequencers, DNA arrays, and mass spectrometry for omics studies. Among a number of analytical tools and technologies, CE and CE coupled to MS are promising and relatively rapidly developing tools with the potential to provide qualitative and quantitative analyses of biological molecules. With an emphasis on CE for systems biology, this review summarizes the method developments and applications of CE for the genomic, transcriptomic, proteomic, and metabolomic studies focusing on the drug discovery and disease diagnosis and therapies since 2009. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative assessment of image motion blur in diffraction images of moving biological cells

NASA Astrophysics Data System (ADS)

Wang, He; Jin, Changrong; Feng, Yuanming; Qi, Dandan; Sa, Yu; Hu, Xin-Hua

2016-02-01

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.

Will Quantitative Proteomics Redefine Some of the Key Concepts in Skeletal Muscle Physiology?

PubMed

Gizak, Agnieszka; Rakus, Dariusz

2016-01-11

Molecular and cellular biology methodology is traditionally based on the reasoning called "the mechanistic explanation". In practice, this means identifying and selecting correlations between biological processes which result from our manipulation of a biological system. In theory, a successful application of this approach requires precise knowledge about all parameters of a studied system. However, in practice, due to the systems' complexity, this requirement is rarely, if ever, accomplished. Typically, it is limited to a quantitative or semi-quantitative measurements of selected parameters (e.g., concentrations of some metabolites), and a qualitative or semi-quantitative description of expression/post-translational modifications changes within selected proteins. A quantitative proteomics approach gives a possibility of quantitative characterization of the entire proteome of a biological system, in the context of the titer of proteins as well as their post-translational modifications. This enables not only more accurate testing of novel hypotheses but also provides tools that can be used to verify some of the most fundamental dogmas of modern biology. In this short review, we discuss some of the consequences of using quantitative proteomics to verify several key concepts in skeletal muscle physiology.

Quantitative characterization of genetic parts and circuits for plant synthetic biology.

PubMed

Schaumberg, Katherine A; Antunes, Mauricio S; Kassaw, Tessema K; Xu, Wenlong; Zalewski, Christopher S; Medford, June I; Prasad, Ashok

2016-01-01

Plant synthetic biology promises immense technological benefits, including the potential development of a sustainable bio-based economy through the predictive design of synthetic gene circuits. Such circuits are built from quantitatively characterized genetic parts; however, this characterization is a significant obstacle in work with plants because of the time required for stable transformation. We describe a method for rapid quantitative characterization of genetic plant parts using transient expression in protoplasts and dual luciferase outputs. We observed experimental variability in transient-expression assays and developed a mathematical model to describe, as well as statistical normalization methods to account for, this variability, which allowed us to extract quantitative parameters. We characterized >120 synthetic parts in Arabidopsis and validated our method by comparing transient expression with expression in stably transformed plants. We also tested >100 synthetic parts in sorghum (Sorghum bicolor) protoplasts, and the results showed that our method works in diverse plant groups. Our approach enables the construction of tunable gene circuits in complex eukaryotic organisms.

Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk

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

Costa, Pedro M.; Fadeel, Bengt, E-mail: Bengt.Fade

Engineered nanomaterials are being developed for a variety of technological applications. However, the increasing use of nanomaterials in society has led to concerns about their potential adverse effects on human health and the environment. During the first decade of nanotoxicological research, the realization has emerged that effective risk assessment of the multitudes of new nanomaterials would benefit from a comprehensive understanding of their toxicological mechanisms, which is difficult to achieve with traditional, low-throughput, single end-point oriented approaches. Therefore, systems biology approaches are being progressively applied within the nano(eco)toxicological sciences. This novel paradigm implies that the study of biological systems shouldmore » be integrative resulting in quantitative and predictive models of nanomaterial behaviour in a biological system. To this end, global ‘omics’ approaches with which to assess changes in genes, proteins, metabolites, etc. are deployed allowing for computational modelling of the biological effects of nanomaterials. Here, we highlight omics and systems biology studies in nanotoxicology, aiming towards the implementation of a systems nanotoxicology and mechanism-based risk assessment of nanomaterials. - Highlights: • Systems nanotoxicology is a multi-disciplinary approach to quantitative modelling. • Transcriptomics, proteomics and metabolomics remain the most common methods. • Global “omics” techniques should be coupled to computational modelling approaches. • The discovery of nano-specific toxicity pathways and biomarkers is a prioritized goal. • Overall, experimental nanosafety research must endeavour reproducibility and relevance.« less

Computational Systems Biology

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

McDermott, Jason E.; Samudrala, Ram; Bumgarner, Roger E.

2009-05-01

Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (“systems”) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing and computational processing power has driven a reinvention and expansion of this field. The expansions include not only modeling of small metabolic{Ishii, 2004 #1129; Ekins, 2006 #1601; Lafaye, 2005 #1744} and signaling systems{Stevenson-Paulik, 2006 #1742; Lafaye, 2005more » #1744} but also modeling of the relationships between biological components in very large systems, incluyding whole cells and organisms {Ideker, 2001 #1124; Pe'er, 2001 #1172; Pilpel, 2001 #393; Ideker, 2002 #327; Kelley, 2003 #1117; Shannon, 2003 #1116; Ideker, 2004 #1111}{Schadt, 2003 #475; Schadt, 2006 #1661}{McDermott, 2002 #878; McDermott, 2005 #1271}. Generally these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships and general features that are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (for example, the identification of an evolutionary pattern), or are interesting and valuable to researchers working on a particular problem (for example highlight a previously unknown functional pathway). Two events have occurred to bring about the field computational systems biology to the forefront. One is the advent of high throughput methods that have generated large amounts of information about particular systems in the form of genetic studies, gene expression analyses (both

Biological conversion system

DOEpatents

Scott, C.D.

A system for bioconversion of organic material comprises a primary bioreactor column wherein a biological active agent (zymomonas mobilis) converts the organic material (sugar) to a product (alcohol), a rejuvenator column wherein the biological activity of said biological active agent is enhanced, and means for circulating said biological active agent between said primary bioreactor column and said rejuvenator column.

Gender, Math Confidence, and Grit: Relationships with Quantitative Skills and Performance in an Undergraduate Biology Course.

PubMed

Flanagan, K M; Einarson, J

2017-01-01

In a world filled with big data, mathematical models, and statistics, the development of strong quantitative skills is becoming increasingly critical for modern biologists. Teachers in this field must understand how students acquire quantitative skills and explore barriers experienced by students when developing these skills. In this study, we examine the interrelationships among gender, grit, and math confidence for student performance on a pre-post quantitative skills assessment and overall performance in an undergraduate biology course. Here, we show that females significantly underperformed relative to males on a quantitative skills assessment at the start of term. However, females showed significantly higher gains over the semester, such that the gender gap in performance was nearly eliminated by the end of the semester. Math confidence plays an important role in the performance on both the pre and post quantitative skills assessments and overall performance in the course. The effect of grit on student performance, however, is mediated by a student's math confidence; as math confidence increases, the positive effect of grit decreases. Consequently, the positive impact of a student's grittiness is observed most strongly for those students with low math confidence. We also found grit to be positively associated with the midterm score and the final grade in the course. Given the relationships established in this study among gender, grit, and math confidence, we provide "instructor actions" from the literature that can be applied in the classroom to promote the development of quantitative skills in light of our findings. © 2017 K. M. Flanagan and J. Einarson. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http

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

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 physical and biological basis of quantitative parameters derived from diffusion MRI

PubMed Central

2012-01-01

Diffusion magnetic resonance imaging is a quantitative imaging technique that measures the underlying molecular diffusion of protons. Diffusion-weighted imaging (DWI) quantifies the apparent diffusion coefficient (ADC) which was first used to detect early ischemic stroke. However this does not take account of the directional dependence of diffusion seen in biological systems (anisotropy). Diffusion tensor imaging (DTI) provides a mathematical model of diffusion anisotropy and is widely used. Parameters, including fractional anisotropy (FA), mean diffusivity (MD), parallel and perpendicular diffusivity can be derived to provide sensitive, but non-specific, measures of altered tissue structure. They are typically assessed in clinical studies by voxel-based or region-of-interest based analyses. The increasing recognition of the limitations of the diffusion tensor model has led to more complex multi-compartment models such as CHARMED, AxCaliber or NODDI being developed to estimate microstructural parameters including axonal diameter, axonal density and fiber orientations. However these are not yet in routine clinical use due to lengthy acquisition times. In this review, I discuss how molecular diffusion may be measured using diffusion MRI, the biological and physical bases for the parameters derived from DWI and DTI, how these are used in clinical studies and the prospect of more complex tissue models providing helpful micro-structural information. PMID:23289085

Quantitative characterization of nanoparticle agglomeration within biological media

NASA Astrophysics Data System (ADS)

Hondow, Nicole; Brydson, Rik; Wang, Peiyi; Holton, Mark D.; Brown, M. Rowan; Rees, Paul; Summers, Huw D.; Brown, Andy

2012-07-01

Quantitative analysis of nanoparticle dispersion state within biological media is essential to understanding cellular uptake and the roles of diffusion, sedimentation, and endocytosis in determining nanoparticle dose. The dispersion of polymer-coated CdTe/ZnS quantum dots in water and cell growth medium with and without fetal bovine serum was analyzed by transmission electron microscopy (TEM) and dynamic light scattering (DLS) techniques. Characterization by TEM of samples prepared by plunge freezing the blotted solutions into liquid ethane was sensitive to the dispersion state of the quantum dots and enabled measurement of agglomerate size distributions even in the presence of serum proteins where DLS failed. In addition, TEM showed a reduced packing fraction of quantum dots per agglomerate when dispersed in biological media and serum compared to just water, highlighting the effect of interactions between the media, serum proteins, and the quantum dots. The identification of a heterogeneous distribution of quantum dots and quantum dot agglomerates in cell growth medium and serum by TEM will enable correlation with the previously reported optical metrology of in vitro cellular uptake of this quantum dot dispersion. In this paper, we present a comparative study of TEM and DLS and show that plunge-freeze TEM provides a robust assessment of nanoparticle agglomeration state.

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

Hierarchical structure of biological systems

PubMed Central

Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

2014-01-01

A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961

Closing the Loop: Involving Faculty in the Assessment of Scientific and Quantitative Reasoning Skills of Biology Majors

ERIC Educational Resources Information Center

Hurney, Carol A.; Brown, Justin; Griscom, Heather Peckham; Kancler, Erika; Wigtil, Clifton J.; Sundre, Donna

2011-01-01

The development of scientific and quantitative reasoning skills in undergraduates majoring in science, technology, engineering, and mathematics (STEM) is an objective of many courses and curricula. The Biology Department at James Madison University (JMU) assesses these essential skills in graduating biology majors by using a multiple-choice exam…

Life at the Common Denominator: Mechanistic and Quantitative Biology for the Earth and Space Sciences

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.

2010-01-01

The remarkable challenges and possibilities of the coming few decades will compel the biogeochemical and astrobiological sciences to characterize the interactions between biology and its environment in a fundamental, mechanistic, and quantitative fashion. The clear need for integrative and scalable biology-environment models is exemplified in the Earth sciences by the challenge of effectively addressing anthropogenic global change, and in the space sciences by the challenge of mounting a well-constrained yet sufficiently adaptive and inclusive search for life beyond Earth. Our understanding of the life-planet interaction is still, however, largely empirical. A variety of approaches seek to move from empirical to mechanistic descriptions. One approach focuses on the relationship between biology and energy, which is at once universal (all life requires energy), unique (life manages energy flow in a fashion not seen in abiotic systems), and amenable to characterization and quantification in thermodynamic terms. Simultaneously, a focus on energy flow addresses a critical point of interface between life and its geological, chemical, and physical environment. Characterizing and quantifying this relationship for life on Earth will support the development of integrative and predictive models for biology-environment dynamics. Understanding this relationship at its most fundamental level holds potential for developing concepts of habitability and biosignatures that can optimize astrobiological exploration strategies and are extensible to all life.

Connecting qualitative observation and quantitative measurement for enhancing quantitative literacy in plant anatomy course

NASA Astrophysics Data System (ADS)

Nuraeni, E.; Rahmat, A.

2018-05-01

Forming of cognitive schemes of plant anatomy concepts is performed by processing of qualitative and quantitative data obtained from microscopic observations. To enhancing student’s quantitative literacy, strategy of plant anatomy course was modified by adding the task to analyze quantitative data produced by quantitative measurement of plant anatomy guided by material course. Participant in this study was 24 biology students and 35 biology education students. Quantitative Literacy test, complex thinking in plant anatomy test and questioner used to evaluate the course. Quantitative literacy capability data was collected by quantitative literacy test with the rubric from the Association of American Colleges and Universities, Complex thinking in plant anatomy by test according to Marzano and questioner. Quantitative literacy data are categorized according to modified Rhodes and Finley categories. The results showed that quantitative literacy of biology education students is better than biology students.

Endobiogeny: a global approach to systems biology (part 1 of 2).

PubMed

Lapraz, Jean-Claude; Hedayat, Kamyar M

2013-01-01

Endobiogeny is a global systems approach to human biology that may offer an advancement in clinical medicine based in scientific principles of rigor and experimentation and the humanistic principles of individualization of care and alleviation of suffering with minimization of harm. Endobiogeny is neither a movement away from modern science nor an uncritical embracing of pre-rational methods of inquiry but a synthesis of quantitative and qualitative relationships reflected in a systems-approach to life and based on new mathematical paradigms of pattern recognition.

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

PubMed

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

2010-02-01

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell

Plant systems biology: network matters.

PubMed

Lucas, Mikaël; Laplaze, Laurent; Bennett, Malcolm J

2011-04-01

Systems biology is all about networks. A recent trend has been to associate systems biology exclusively with the study of gene regulatory or protein-interaction networks. However, systems biology approaches can be applied at many other scales, from the subatomic to the ecosystem scales. In this review, we describe studies at the sub-cellular, tissue, whole plant and crop scales and highlight how these studies can be related to systems biology. We discuss the properties of system approaches at each scale as well as their current limits, and pinpoint in each case advances unique to the considered scale but representing potential for the other scales. We conclude by examining plant models bridging different scales and considering the future prospects of plant systems biology. © 2011 Blackwell Publishing Ltd.

Informing Biological Design by Integration of Systems and Synthetic Biology

PubMed Central

Smolke, Christina D.; Silver, Pamela A.

2011-01-01

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

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.

Automated quantitative assessment of proteins' biological function in protein knowledge bases.

PubMed

Mayr, Gabriele; Lepperdinger, Günter; Lackner, Peter

2008-01-01

Primary protein sequence data are archived in databases together with information regarding corresponding biological functions. In this respect, UniProt/Swiss-Prot is currently the most comprehensive collection and it is routinely cross-examined when trying to unravel the biological role of hypothetical proteins. Bioscientists frequently extract single entries and further evaluate those on a subjective basis. In lieu of a standardized procedure for scoring the existing knowledge regarding individual proteins, we here report about a computer-assisted method, which we applied to score the present knowledge about any given Swiss-Prot entry. Applying this quantitative score allows the comparison of proteins with respect to their sequence yet highlights the comprehension of functional data. pfs analysis may be also applied for quality control of individual entries or for database management in order to rank entry listings.

Micro-separation toward systems biology.

PubMed

Liu, Bi-Feng; Xu, Bo; Zhang, Guisen; Du, Wei; Luo, Qingming

2006-02-17

Current biology is experiencing transformation in logic or philosophy that forces us to reevaluate the concept of cell, tissue or entire organism as a collection of individual components. Systems biology that aims at understanding biological system at the systems level is an emerging research area, which involves interdisciplinary collaborations of life sciences, computational and mathematical sciences, systems engineering, and analytical technology, etc. For analytical chemistry, developing innovative methods to meet the requirement of systems biology represents new challenges as also opportunities and responsibility. In this review, systems biology-oriented micro-separation technologies are introduced for comprehensive profiling of genome, proteome and metabolome, characterization of biomolecules interaction and single cell analysis such as capillary electrophoresis, ultra-thin layer gel electrophoresis, micro-column liquid chromatography, and their multidimensional combinations, parallel integrations, microfabricated formats, and nano technology involvement. Future challenges and directions are also suggested.

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.

Method for photo-altering a biological system to improve biological effect

DOEpatents

Hill, Richard A.; Doiron, Daniel R.; Crean, David H.

2000-08-01

Photodynamic therapy is a new adjunctive therapy for filtration surgery that does not use chemotherapy agents or radiation, but uses pharmacologically-active sensitizing compounds to produce a titratable, localized, transient, post operative avascular conjunctiva. A photosensitizing agent in a biological system is selectively activated by delivering the photosensitive agent to the biological system and laser activating only a spatially selected portion of the delivered photosensitive agent. The activated portion of the photosensitive agent reacts with the biological system to obtain a predetermined biological effect. As a result, an improved spatial disposition and effectuation of the biological effect by the photosensitive agent in the biological system is achieved.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological

Systems biology and mechanics of growth.

PubMed

Eskandari, Mona; Kuhl, Ellen

2015-01-01

In contrast to inert systems, living biological systems have the advantage to adapt to their environment through growth and evolution. This transfiguration is evident during embryonic development, when the predisposed need to grow allows form to follow function. Alterations in the equilibrium state of biological systems breed disease and mutation in response to environmental triggers. The need to characterize the growth of biological systems to better understand these phenomena has motivated the continuum theory of growth and stimulated the development of computational tools in systems biology. Biological growth in development and disease is increasingly studied using the framework of morphoelasticity. Here, we demonstrate the potential for morphoelastic simulations through examples of volume, area, and length growth, inspired by tumor expansion, chronic bronchitis, brain development, intestine formation, plant shape, and myopia. We review the systems biology of living systems in light of biochemical and optical stimuli and classify different types of growth to facilitate the design of growth models for various biological systems within this generic framework. Exploring the systems biology of growth introduces a new venue to control and manipulate embryonic development, disease progression, and clinical intervention. © 2015 Wiley Periodicals, Inc.

System-Wide Quantitative Proteomics of the Metabolic Syndrome in Mice: Genotypic and Dietary Effects.

PubMed

Terfve, Camille; Sabidó, Eduard; Wu, Yibo; Gonçalves, Emanuel; Choi, Meena; Vaga, Stefania; Vitek, Olga; Saez-Rodriguez, Julio; Aebersold, Ruedi

2017-02-03

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although the deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used an experimental design with two different mouse strains and dietary and metabolic perturbations to generate a compendium of quantitative proteome data using three mass spectrometric techniques. The data reproduce known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic data sets provide an unbiased broad overview of the behavior of complex systems after perturbation.

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

‘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

Quantitative evolutionary design

PubMed Central

Diamond, Jared

2002-01-01

The field of quantitative evolutionary design uses evolutionary reasoning (in terms of natural selection and ultimate causation) to understand the magnitudes of biological reserve capacities, i.e. excesses of capacities over natural loads. Ratios of capacities to loads, defined as safety factors, fall in the range 1.2-10 for most engineered and biological components, even though engineered safety factors are specified intentionally by humans while biological safety factors arise through natural selection. Familiar examples of engineered safety factors include those of buildings, bridges and elevators (lifts), while biological examples include factors of bones and other structural elements, of enzymes and transporters, and of organ metabolic performances. Safety factors serve to minimize the overlap zone (resulting in performance failure) between the low tail of capacity distributions and the high tail of load distributions. Safety factors increase with coefficients of variation of load and capacity, with capacity deterioration with time, and with cost of failure, and decrease with costs of initial construction, maintenance, operation, and opportunity. Adaptive regulation of many biological systems involves capacity increases with increasing load; several quantitative examples suggest sublinear increases, such that safety factors decrease towards 1.0. Unsolved questions include safety factors of series systems, parallel or branched pathways, elements with multiple functions, enzyme reaction chains, and equilibrium enzymes. The modest sizes of safety factors imply the existence of costs that penalize excess capacities. Those costs are likely to involve wasted energy or space for large or expensive components, but opportunity costs of wasted space at the molecular level for minor components. PMID:12122135

Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration.

PubMed

Sauro, Herbert M; Hucka, Michael; Finney, Andrew; Wellock, Cameron; Bolouri, Hamid; Doyle, John; Kitano, Hiroaki

2003-01-01

Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.

Selection on quantitative colour variation in Centaurea cyanus: the role of the pollinator's visual system.

PubMed

Renoult, J P; Thomann, M; Schaefer, H M; Cheptou, P-O

2013-11-01

Even though the importance of selection for trait evolution is well established, we still lack a functional understanding of the mechanisms underlying phenotypic selection. Because animals necessarily use their sensory system to perceive phenotypic traits, the model of sensory bias assumes that sensory systems are the main determinant of signal evolution. Yet, it has remained poorly known how sensory systems contribute to shaping the fitness surface of selected individuals. In a greenhouse experiment, we quantified the strength and direction of selection on floral coloration in a population of cornflowers exposed to bumblebees as unique pollinators during 4 days. We detected significant selection on the chromatic and achromatic (brightness) components of floral coloration. We then studied whether these patterns of selection are explicable by accounting for the visual system of the pollinators. Using data on bumblebee colour vision, we first showed that bumblebees should discriminate among quantitative colour variants. The observed selection was then compared to the selection predicted by psychophysical models of bumblebee colour vision. The achromatic but not the chromatic channel of the bumblebee's visual system could explain the observed pattern of selection. These results highlight that (i) pollinators can select quantitative variation in floral coloration and could thus account for a gradual evolution of flower coloration, and (ii) stimulation of the visual system represents, at least partly, a functional mechanism potentially explaining pollinators' selection on floral colour variants. © 2013 The Authors. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

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.

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.

Quantitation of zolpidem in biological fluids by electro-driven microextraction combined with HPLC-UV analysis.

PubMed

Yaripour, Saeid; Mohammadi, Ali; Esfanjani, Isa; Walker, Roderick B; Nojavan, Saeed

2018-01-01

In this study, for the first time, an electro-driven microextraction method named electromembrane extraction combined with a simple high performance liquid chromatography and ultraviolet detection was developed and validated for the quantitation of zolpidem in biological samples. Parameters influencing electromembrane extraction were evaluated and optimized. The membrane consisted of 2-ethylhexanol immobilized in the pores of a hollow fiber. As a driving force, a 150 V electric field was applied to facilitate the analyte migration from the sample matrix to an acceptor solution through a supported liquid membrane. The pHs of donor and acceptor solutions were optimized to 6.0 and 2.0, respectively. The enrichment factor was obtained >75 within 15 minutes. The effect of carbon nanotubes (as solid nano-sorbents) on the membrane performance and EME efficiency was evaluated. The method was linear over the range of 10-1000 ng/mL for zolpidem (R 2 >0.9991) with repeatability ( %RSD) between 0.3 % and 7.3 % ( n = 3). The limits of detection and quantitation were 3 and 10 ng/mL, respectively. The sensitivity of HPLC-UV for the determination of zolpidem was enhanced by electromembrane extraction. Finally, the method was employed for the quantitation of zolpidem in biological samples with relative recoveries in the range of 60-79 %.

A Quantitative Approach to Assessing System Evolvability

NASA Technical Reports Server (NTRS)

Christian, John A., III

2004-01-01

When selecting a system from multiple candidates, the customer seeks the one that best meets his or her needs. Recently the desire for evolvable systems has become more important and engineers are striving to develop systems that accommodate this need. In response to this search for evolvability, we present a historical perspective on evolvability, propose a refined definition of evolvability, and develop a quantitative method for measuring this property. We address this quantitative methodology from both a theoretical and practical perspective. This quantitative model is then applied to the problem of evolving a lunar mission to a Mars mission as a case study.

A basis for a visual language for describing, archiving and analyzing functional models of complex biological systems

PubMed Central

Cook, Daniel L; Farley, Joel F; Tapscott, Stephen J

2001-01-01

Background: We propose that a computerized, internet-based graphical description language for systems biology will be essential for describing, archiving and analyzing complex problems of biological function in health and disease. Results: We outline here a conceptual basis for designing such a language and describe BioD, a prototype language that we have used to explore the utility and feasibility of this approach to functional biology. Using example models, we demonstrate that a rather limited lexicon of icons and arrows suffices to describe complex cell-biological systems as discrete models that can be posted and linked on the internet. Conclusions: Given available computer and internet technology, BioD may be implemented as an extensible, multidisciplinary language that can be used to archive functional systems knowledge and be extended to support both qualitative and quantitative functional analysis. PMID:11305940

Quantitative model analysis with diverse biological data: applications in developmental pattern formation.

PubMed

Pargett, Michael; Umulis, David M

2013-07-15

Mathematical modeling of transcription factor and signaling networks is widely used to understand if and how a mechanism works, and to infer regulatory interactions that produce a model consistent with the observed data. Both of these approaches to modeling are informed by experimental data, however, much of the data available or even acquirable are not quantitative. Data that is not strictly quantitative cannot be used by classical, quantitative, model-based analyses that measure a difference between the measured observation and the model prediction for that observation. To bridge the model-to-data gap, a variety of techniques have been developed to measure model "fitness" and provide numerical values that can subsequently be used in model optimization or model inference studies. Here, we discuss a selection of traditional and novel techniques to transform data of varied quality and enable quantitative comparison with mathematical models. This review is intended to both inform the use of these model analysis methods, focused on parameter estimation, and to help guide the choice of method to use for a given study based on the type of data available. Applying techniques such as normalization or optimal scaling may significantly improve the utility of current biological data in model-based study and allow greater integration between disparate types of data. Copyright © 2013 Elsevier Inc. All rights reserved.

Biophysics and systems biology.

PubMed

Noble, Denis

2010-03-13

Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights.

Biophysics and systems biology

PubMed Central

Noble, Denis

2010-01-01

Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights. PMID:20123750

A method for operative quantitative interpretation of multispectral images of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-10-01

A method for operative retrieval of spatial distributions of biophysical parameters of a biological tissue by using a multispectral image of it has been developed. The method is based on multiple regressions between linearly independent components of the diffuse reflection spectrum of the tissue and unknown parameters. Possibilities of the method are illustrated by an example of determining biophysical parameters of the skin (concentrations of melanin, hemoglobin and bilirubin, blood oxygenation, and scattering coefficient of the tissue). Examples of quantitative interpretation of the experimental data are presented.

Quantitative risk assessment system (QRAS)

NASA Technical Reports Server (NTRS)

Tan, Zhibin (Inventor); Mosleh, Ali (Inventor); Weinstock, Robert M (Inventor); Smidts, Carol S (Inventor); Chang, Yung-Hsien (Inventor); Groen, Francisco J (Inventor); Swaminathan, Sankaran (Inventor)

2001-01-01

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.

Institute for Genomics and Systems Biology

Science.gov Websites

Institute for Genomics and Systems Biology Discover. Predict. Improve. Advancing Human and , 2015 See all Research Papers Featured Video Introduction to Systems Biology Video: Introduction to Systems Biology News Jack Gilbert Heading UChicago Startup that Aims to Predict Behavior of Trillions of

Introduction to the Symposium "Leading Students and Faculty to Quantitative Biology through Active Learning".

PubMed

Waldrop, Lindsay D; Miller, Laura A

2015-11-01

The broad aim of this symposium and set of associated papers is to motivate the use of inquiry-based, active-learning teaching techniques in undergraduate quantitative biology courses. Practical information, resources, and ready-to-use classroom exercises relevant to physicists, mathematicians, biologists, and engineers are presented. These resources can be used to address the lack of preparation of college students in STEM fields entering the workforce by providing experience working on interdisciplinary and multidisciplinary problems in mathematical biology in a group setting. Such approaches can also indirectly help attract and retain under-represented students who benefit the most from "non-traditional" learning styles and strategies, including inquiry-based, collaborative, and active learning. © The Author 2015. 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.

Scattering matrix elements of biological particles measured in a flow through system: theory and practice.

PubMed

Sloot, P M; Hoekstra, A G; van der Liet, H; Figdor, C G

1989-05-15

Light scattering techniques (including depolarization experiments) applied to biological cells provide a fast nondestructive probe that is very sensitive to small morphological differences. Until now quantitative measurement of these scatter phenomena were only described for particles in suspension. In this paper we discuss the symmetry conditions applicable to the scattering matrices of monodisperse biological cells in a flow cytometer and provide evidence that quantitative measurement of the elements of these scattering matrices is possible in flow through systems. Two fundamental extensions to the theoretical description of conventional scattering experiments are introduced: large cone integration of scattering signals and simultaneous implementation of the localization principle to account for scattering by a sharply focused laser beam. In addition, a specific calibration technique is proposed to account for depolarization effects of the highly specialized optics applied in flow through equipment.

Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice.

PubMed

Antoch, Marina P; Wrobel, Michelle; Kuropatwinski, Karen K; Gitlin, Ilya; Leonova, Katerina I; Toshkov, Ilia; Gleiberman, Anatoli S; Hutson, Alan D; Chernova, Olga B; Gudkov, Andrei V

2017-03-19

The development of healthspan-extending pharmaceuticals requires quantitative estimation of age-related progressive physiological decline. In humans, individual health status can be quantitatively assessed by means of a frailty index (FI), a parameter which reflects the scale of accumulation of age-related deficits. However, adaptation of this methodology to animal models is a challenging task since it includes multiple subjective parameters. Here we report a development of a quantitative non-invasive procedure to estimate biological age of an individual animal by creating physiological frailty index (PFI). We demonstrated the dynamics of PFI increase during chronological aging of male and female NIH Swiss mice. We also demonstrated acceleration of growth of PFI in animals placed on a high fat diet, reflecting aging acceleration by obesity and provide a tool for its quantitative assessment. Additionally, we showed that PFI could reveal anti-aging effect of mTOR inhibitor rapatar (bioavailable formulation of rapamycin) prior to registration of its effects on longevity. PFI revealed substantial sex-related differences in normal chronological aging and in the efficacy of detrimental (high fat diet) or beneficial (rapatar) aging modulatory factors. Together, these data introduce PFI as a reliable, non-invasive, quantitative tool suitable for testing potential anti-aging pharmaceuticals in pre-clinical studies.

The Silicon Trypanosome: a test case of iterative model extension in systems biology

PubMed Central

Achcar, Fiona; Fadda, Abeer; Haanstra, Jurgen R.; Kerkhoven, Eduard J.; Kim, Dong-Hyun; Leroux, Alejandro E.; Papamarkou, Theodore; Rojas, Federico; Bakker, Barbara M.; Barrett, Michael P.; Clayton, Christine; Girolami, Mark; Luise Krauth-Siegel, R.; Matthews, Keith R.; Breitling, Rainer

2016-01-01

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome (SilicoTryp) is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology. PMID:24797926

Morphomics: An integral part of systems biology of the human placenta.

PubMed

Mayhew, T M

2015-04-01

The placenta is a transient organ the functioning of which has health consequences far beyond the embryo/fetus. Understanding the biology of any system (organ, organism, single cell, etc) requires a comprehensive and inclusive approach which embraces all the biomedical disciplines and 'omic' technologies and then integrates information obtained from all of them. Among the latest 'omics' is morphomics. The terms morphome and morphomics have been applied incoherently in biology and biomedicine but, recently, they have been given clear and widescale definitions. Morphomics is placed in the context of other 'omics' and its pertinent technologies and tools for sampling and quantitation are reviewed. Emphasis is accorded to the importance of random sampling principles in systems biology and the value of combining 3D quantification with alternative imaging techniques to advance knowledge and understanding of the human placental morphome. By analogy to other 'omes', the morphome is the totality of morphological features within a system and morphomics is the systematic study of those structures. Information about structure is required at multiple levels of resolution in order to understand better the processes by which a given system alters with time, experimental treatment or environmental insult. Therefore, morphomics research includes all imaging techniques at all levels of achievable resolution from gross anatomy and medical imaging, via optical and electron microscopy, to molecular characterisation. Quantification is an important element of all 'omics' studies and, because biological systems exist and operate in 3-dimensional (3D) space, precise descriptions of form, content and spatial relationships require the quantification of structure in 3D. These considerations are relevant to future study contributions to the Human Placenta Project. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantitation of zolpidem in biological fluids by electro-driven microextraction combined with HPLC-UV analysis

PubMed Central

Yaripour, Saeid; Mohammadi, Ali; Esfanjani, Isa; Walker, Roderick B.; Nojavan, Saeed

2018-01-01

In this study, for the first time, an electro-driven microextraction method named electromembrane extraction combined with a simple high performance liquid chromatography and ultraviolet detection was developed and validated for the quantitation of zolpidem in biological samples. Parameters influencing electromembrane extraction were evaluated and optimized. The membrane consisted of 2-ethylhexanol immobilized in the pores of a hollow fiber. As a driving force, a 150 V electric field was applied to facilitate the analyte migration from the sample matrix to an acceptor solution through a supported liquid membrane. The pHs of donor and acceptor solutions were optimized to 6.0 and 2.0, respectively. The enrichment factor was obtained >75 within 15 minutes. The effect of carbon nanotubes (as solid nano-sorbents) on the membrane performance and EME efficiency was evaluated. The method was linear over the range of 10-1000 ng/mL for zolpidem (R2 >0.9991) with repeatability ( %RSD) between 0.3 % and 7.3 % (n = 3). The limits of detection and quantitation were 3 and 10 ng/mL, respectively. The sensitivity of HPLC-UV for the determination of zolpidem was enhanced by electromembrane extraction. Finally, the method was employed for the quantitation of zolpidem in biological samples with relative recoveries in the range of 60-79 %. PMID:29805344

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

Calculating life? Duelling discourses in interdisciplinary systems biology.

PubMed

Calvert, Jane; Fujimura, Joan H

2011-06-01

A high profile context in which physics and biology meet today is in the new field of systems biology. Systems biology is a fascinating subject for sociological investigation because the demands of interdisciplinary collaboration have brought epistemological issues and debates front and centre in discussions amongst systems biologists in conference settings, in publications, and in laboratory coffee rooms. One could argue that systems biologists are conducting their own philosophy of science. This paper explores the epistemic aspirations of the field by drawing on interviews with scientists working in systems biology, attendance at systems biology conferences and workshops, and visits to systems biology laboratories. It examines the discourses of systems biologists, looking at how they position their work in relation to previous types of biological inquiry, particularly molecular biology. For example, they raise the issue of reductionism to distinguish systems biology from molecular biology. This comparison with molecular biology leads to discussions about the goals and aspirations of systems biology, including epistemic commitments to quantification, rigor and predictability. Some systems biologists aspire to make biology more similar to physics and engineering by making living systems calculable, modelable and ultimately predictable-a research programme that is perhaps taken to its most extreme form in systems biology's sister discipline: synthetic biology. Other systems biologists, however, do not think that the standards of the physical sciences are the standards by which we should measure the achievements of systems biology, and doubt whether such standards will ever be applicable to 'dirty, unruly living systems'. This paper explores these epistemic tensions and reflects on their sociological dimensions and their consequences for future work in the life sciences. Copyright © 2010 Elsevier Ltd. All rights reserved.

Universally Sloppy Parameter Sensitivities in Systems Biology Models

PubMed Central

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-01-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a “sloppy” spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters. PMID:17922568

Universally sloppy parameter sensitivities in systems biology models.

PubMed

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Digital biology and chemistry.

PubMed

Witters, Daan; Sun, Bing; Begolo, Stefano; Rodriguez-Manzano, Jesus; Robles, Whitney; Ismagilov, Rustem F

2014-09-07

This account examines developments in "digital" biology and chemistry within the context of microfluidics, from a personal perspective. Using microfluidics as a frame of reference, we identify two areas of research within digital biology and chemistry that are of special interest: (i) the study of systems that switch between discrete states in response to changes in chemical concentration of signals, and (ii) the study of single biological entities such as molecules or cells. In particular, microfluidics accelerates analysis of switching systems (i.e., those that exhibit a sharp change in output over a narrow range of input) by enabling monitoring of multiple reactions in parallel over a range of concentrations of signals. Conversely, such switching systems can be used to create new kinds of microfluidic detection systems that provide "analog-to-digital" signal conversion and logic. Microfluidic compartmentalization technologies for studying and isolating single entities can be used to reconstruct and understand cellular processes, study interactions between single biological entities, and examine the intrinsic heterogeneity of populations of molecules, cells, or organisms. Furthermore, compartmentalization of single cells or molecules in "digital" microfluidic experiments can induce switching in a range of reaction systems to enable sensitive detection of cells or biomolecules, such as with digital ELISA or digital PCR. This "digitizing" offers advantages in terms of robustness, assay design, and simplicity because quantitative information can be obtained with qualitative measurements. While digital formats have been shown to improve the robustness of existing chemistries, we anticipate that in the future they will enable new chemistries to be used for quantitative measurements, and that digital biology and chemistry will continue to provide further opportunities for measuring biomolecules, understanding natural systems more deeply, and advancing molecular and

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.

PubMed

Allen, R J; Rieger, T R; Musante, C J

2016-03-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

PubMed Central

Rieger, TR; Musante, CJ

2016-01-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed “virtual patients.” In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations. PMID:27069777

Systems biology of human atherosclerosis.

PubMed

Shalhoub, Joseph; Sikkel, Markus B; Davies, Kerry J; Vorkas, Panagiotis A; Want, Elizabeth J; Davies, Alun H

2014-01-01

Systems biology describes a holistic and integrative approach to understand physiology and pathology. The "omic" disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the "net" biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism's context. Systems biology relies on the existence of "nodes" and "edges." Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.

Systems Biology Approach in Hypertension Research.

PubMed

Delles, Christian; Husi, Holger

2017-01-01

Systems biology is an approach to study all genes, gene transcripts, proteins, metabolites, and their interactions in specific cells, tissues, organs, or the whole organism. It is based on data derived from high-throughput analytical technologies and bioinformatics tools to analyze these data, and aims to understand the whole system rather than individual aspects of it. Systems biology can be applied to virtually all conditions and diseases and therefore also to hypertension and its underlying vascular disorders. Unlike other methods in this book there is no clear-cut protocol to explain a systems biology approach. We will instead outline some of the most important and common steps in the generation and analysis of systems biology data.

Agent-based re-engineering of ErbB signaling: a modeling pipeline for integrative systems biology.

PubMed

Das, Arya A; Ajayakumar Darsana, T; Jacob, Elizabeth

2017-03-01

Experiments in systems biology are generally supported by a computational model which quantitatively estimates the parameters of the system by finding the best fit to the experiment. Mathematical models have proved to be successful in reverse engineering the system. The data generated is interpreted to understand the dynamics of the underlying phenomena. The question we have sought to answer is that - is it possible to use an agent-based approach to re-engineer a biological process, making use of the available knowledge from experimental and modelling efforts? Can the bottom-up approach benefit from the top-down exercise so as to create an integrated modelling formalism for systems biology? We propose a modelling pipeline that learns from the data given by reverse engineering, and uses it for re-engineering the system, to carry out in-silico experiments. A mathematical model that quantitatively predicts co-expression of EGFR-HER2 receptors in activation and trafficking has been taken for this study. The pipeline architecture takes cues from the population model that gives the rates of biochemical reactions, to formulate knowledge-based rules for the particle model. Agent-based simulations using these rules, support the existing facts on EGFR-HER2 dynamics. We conclude that, re-engineering models, built using the results of reverse engineering, opens up the possibility of harnessing the power pack of data which now lies scattered in literature. Virtual experiments could then become more realistic when empowered with the findings of empirical cell biology and modelling studies. Implemented on the Agent Modelling Framework developed in-house. C ++ code templates available in Supplementary material . liz.csir@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Abstracts of papers presented at the LVIII Cold Spring Harbor Symposium on quantitative Biology: DNA and chromosomes

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

Not Available

This volume contains the abstracts of oral and poster presentations made at the LVIII Cold Spring Harbor Symposium on Quantitative Biology entitles DNA & Chromosomes. The meeting was held June 2--June 9, 1993 at Cold Spring Harbor, New York.

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.

Quantitative method of medication system interface evaluation.

PubMed

Pingenot, Alleene Anne; Shanteau, James; Pingenot, James D F

2007-01-01

The objective of this study was to develop a quantitative method of evaluating the user interface for medication system software. A detailed task analysis provided a description of user goals and essential activity. A structural fault analysis was used to develop a detailed description of the system interface. Nurses experienced with use of the system under evaluation provided estimates of failure rates for each point in this simplified fault tree. Means of estimated failure rates provided quantitative data for fault analysis. Authors note that, although failures of steps in the program were frequent, participants reported numerous methods of working around these failures so that overall system failure was rare. However, frequent process failure can affect the time required for processing medications, making a system inefficient. This method of interface analysis, called Software Efficiency Evaluation and Fault Identification Method, provides quantitative information with which prototypes can be compared and problems within an interface identified.

Modeling formalisms in Systems Biology

PubMed Central

2011-01-01

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future. PMID:22141422

Mammalian Synthetic Biology: Engineering Biological Systems.

PubMed

Black, Joshua B; Perez-Pinera, Pablo; Gersbach, Charles A

2017-06-21

The programming of new functions into mammalian cells has tremendous application in research and medicine. Continued improvements in the capacity to sequence and synthesize DNA have rapidly increased our understanding of mechanisms of gene function and regulation on a genome-wide scale and have expanded the set of genetic components available for programming cell biology. The invention of new research tools, including targetable DNA-binding systems such as CRISPR/Cas9 and sensor-actuator devices that can recognize and respond to diverse chemical, mechanical, and optical inputs, has enabled precise control of complex cellular behaviors at unprecedented spatial and temporal resolution. These tools have been critical for the expansion of synthetic biology techniques from prokaryotic and lower eukaryotic hosts to mammalian systems. Recent progress in the development of genome and epigenome editing tools and in the engineering of designer cells with programmable genetic circuits is expanding approaches to prevent, diagnose, and treat disease and to establish personalized theranostic strategies for next-generation medicines. This review summarizes the development of these enabling technologies and their application to transforming mammalian synthetic biology into a distinct field in research and medicine.

Systems biology of stored blood cells: can it help to extend the expiration date?

PubMed

Paglia, Giuseppe; Palsson, Bernhard Ø; Sigurjonsson, Olafur E

2012-12-05

With increasingly stringent regulations regarding deferral and elimination of blood donors it will become increasingly important to extend the expiration date of blood components beyond the current allowed storage periods. One reason for the storage time limit for blood components is that platelets and red blood cells develop a condition called storage lesions during their storage in plastic blood containers. Systems biology provides comprehensive bio-chemical descriptions of organisms through quantitative measurements and data integration in mathematical models. The biological knowledge for a target organism can be translated in a mathematical format and used to compute physiological properties. The use of systems biology represents a concrete solution in the study of blood cell storage lesions, and it may open up new avenues towards developing better storage methods and better storage media, thereby extending the storage period of blood components. This article is part of a Special Issue entitled: Integrated omics. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Biological Systems, Energy Sources, and Biology Teaching. Biology and Human Welfare.

ERIC Educational Resources Information Center

Tribe, Michael; Pritchard, Alan J.

This five-chapter document (part of a series on biology and human welfare) focuses on biological systems as energy sources and on the teaching of this subject area. Chapter 1 discusses various topics related to energy and ecology, including biomass, photosynthesis and world energy balances, energy flow through ecosystems, and others. Chapter 2…

Isotope dilution technique for quantitative analysis of endogenous trace element species in biological systems

NASA Astrophysics Data System (ADS)

Schaumlöffel, Dirk; Lobinski, Ryszard

2005-04-01

The aim of this study was to develop an inductively coupled plasma mass spectrometry (ICPMS) method for the determination of enriched species-specific mercury tracers at ng L-1 levels (ppt) in zooplankton and aquatic samples from biological tracer experiments. Applying a cold vapor sector field ICPMS method a high sensitivity was obtained, i.e., 106 cps for 1 [mu]g L-1 of natural mercury measured on 202Hg+, which in turn enabled the measurement of mercury isotope ratios with a 0.6-1.4%R.S.D. precision for a 50 ng L-1 standard. This method was used to quantify CH3201Hg+ and 200Hg2+ tracers in zooplankton from a biological tracer experiment with the aim of investigating the effects of algal density and zooplankton density on mercury bioaccumulation in zooplankton in a fresh water system. For quantification purposes a known amount of 199Hg+ was added to the zooplankton samples before digestion. The digested samples were analyzed and the resulting ICPMS spectra split into four spectra one for each of the four sources of mercury present in the sample (CH3201Hg+, 200Hg2+, 199Hg2+ and natural mercury) using algebraic de-convoluting. The CH3201Hg+ and 200Hg2+ tracers were quantified using an isotope dilution approach with the added 199Hg+. Detection limits were 0.6 and 0.2 ng L-1 for 200Hg+ and CH3201Hg+, respectively. The coefficient of variation on the tracer determinations was approximately 18% CV estimated from the analysis of real samples with tracer concentrations in the <0.1-100 ng L-1 range. The developed method was successfully applied for the determination of species-specific mercury tracers in zooplankton samples from a biological tracer experiment.

Biological and chemical terrorism scenarios and implications for detection systems needs

NASA Astrophysics Data System (ADS)

Gordon, Susanna P.; Chumfong, Isabelle; Edwards, Donna M.; Gleason, Nathaniel J.; West, Todd; Yang, Lynn

2007-04-01

Terrorists intent on causing many deaths and severe disruption to our society could, in theory, cause hundreds to tens of thousands of deaths and significant contamination of key urban facilities by using chemical or biological (CB) agents. The attacks that have occurred to date, such as the 1995 Aum Shinrikyo CB attacks and the 2001 anthrax letters, have been very small on the scale of what is possible. In order to defend against and mitigate the impacts of large-scale terrorist attacks, defensive systems for protection of urban areas and high-value facilities from biological and chemical threats have been deployed. This paper reviews analyses of such scenarios and of the efficacy of potential response options, discusses defensive systems that have been deployed and detectors that are being developed, and finally outlines the detection systems that will be needed for improved CB defense in the future. Sandia's collaboration with San Francisco International Airport on CB defense will also be briefly reviewed, including an overview of airport facility defense guidelines produced in collaboration with Lawrence Berkeley National Laboratory. The analyses that will be discussed were conducted by Sandia National Laboratories' Systems Studies Department in support of the U.S. Department of Homeland Security (DHS) Science and Technology Directorate, and include quantitative analyses utilizing simulation models developed through close collaboration with subject matter experts, such as public health officials in urban areas and biological defense experts.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

PubMed Central

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Sorger, Peter K.; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo

2010-01-01

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. PMID:20186320

Development of capillary electrophoresis methods for quantitative determination of taurine in vehicle system and biological media.

PubMed

da Silva, Dayse L P; Rüttinger, Hans H; Mrestani, Yahia; Baum, Walter F; Neubert, Reinhard H H

2006-06-01

CE methods have been developed for the determination of taurine in pharmaceutical formulation (microemulsion) and in biological media such as sweat. The CE system with end-column pulsed amperometric detection has been found to be an interesting method in comparison with UV and fluorescence detection for its simplicity and rapidity. A gold-disk electrode of 100 mm diameter was used as the working electrode. The effects of a field decoupler at the end of the capillary, separation voltage, injection and pressure times were investigated. A detection limit of 4 x 10(-5) mol/L was reached using integrated pulsed amperometric detection, a method successfully applied to taurine analysis of the biological samples such as sweat. For taurine analysis of oil-in-water microemulsion, fluorescence detector was the favored method, the detection limit of which was 4 x 10(-11) mol/L.

Databases applicable to quantitative hazard/risk assessment-Towards a predictive systems toxicology

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

Waters, Michael; Jackson, Marcus

2008-11-15

The Workshop on The Power of Aggregated Toxicity Data addressed the requirement for distributed databases to support quantitative hazard and risk assessment. The authors have conceived and constructed with federal support several databases that have been used in hazard identification and risk assessment. The first of these databases, the EPA Gene-Tox Database was developed for the EPA Office of Toxic Substances by the Oak Ridge National Laboratory, and is currently hosted by the National Library of Medicine. This public resource is based on the collaborative evaluation, by government, academia, and industry, of short-term tests for the detection of mutagens andmore » presumptive carcinogens. The two-phased evaluation process resulted in more than 50 peer-reviewed publications on test system performance and a qualitative database on thousands of chemicals. Subsequently, the graphic and quantitative EPA/IARC Genetic Activity Profile (GAP) Database was developed in collaboration with the International Agency for Research on Cancer (IARC). A chemical database driven by consideration of the lowest effective dose, GAP has served IARC for many years in support of hazard classification of potential human carcinogens. The Toxicological Activity Profile (TAP) prototype database was patterned after GAP and utilized acute, subchronic, and chronic data from the Office of Air Quality Planning and Standards. TAP demonstrated the flexibility of the GAP format for air toxics, water pollutants and other environmental agents. The GAP format was also applied to developmental toxicants and was modified to represent quantitative results from the rodent carcinogen bioassay. More recently, the authors have constructed: 1) the NIEHS Genetic Alterations in Cancer (GAC) Database which quantifies specific mutations found in cancers induced by environmental agents, and 2) the NIEHS Chemical Effects in Biological Systems (CEBS) Knowledgebase that integrates genomic and other biological data

GeLC-MRM quantitation of mutant KRAS oncoprotein in complex biological samples.

PubMed

Halvey, Patrick J; Ferrone, Cristina R; Liebler, Daniel C

2012-07-06

Tumor-derived mutant KRAS (v-Ki-ras-2 Kirsten rat sarcoma viral oncogene) oncoprotein is a critical driver of cancer phenotypes and a potential biomarker for many epithelial cancers. Targeted mass spectrometry analysis by multiple reaction monitoring (MRM) enables selective detection and quantitation of wild-type and mutant KRAS proteins in complex biological samples. A recently described immunoprecipitation approach (Proc. Nat. Acad. Sci.2011, 108, 2444-2449) can be used to enrich KRAS for MRM analysis, but requires large protein inputs (2-4 mg). Here, we describe sodium dodecyl sulfate-polyacrylamide gel electrophoresis-based enrichment of KRAS in a low molecular weight (20-25 kDa) protein fraction prior to MRM analysis (GeLC-MRM). This approach reduces background proteome complexity, thus, allowing mutant KRAS to be reliably quantified in low protein inputs (5-50 μg). GeLC-MRM detected KRAS mutant variants (G12D, G13D, G12V, G12S) in a panel of cancer cell lines. GeLC-MRM analysis of wild-type and mutant was linear with respect to protein input and showed low variability across process replicates (CV = 14%). Concomitant analysis of a peptide from the highly similar HRAS and NRAS proteins enabled correction of KRAS-targeted measurements for contributions from these other proteins. KRAS peptides were also quantified in fluid from benign pancreatic cysts and pancreatic cancers at concentrations from 0.08 to 1.1 fmol/μg protein. GeLC-MRM provides a robust, sensitive approach to quantitation of mutant proteins in complex biological samples.

A framework for evolutionary systems biology

PubMed Central

Loewe, Laurence

2009-01-01

Background Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects. Results Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism. Conclusion EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications. PMID:19239699

Quantitative phase imaging of biological cells using spatially low and temporally high coherent light source.

PubMed

Ahmad, Azeem; Dubey, Vishesh; Singh, Gyanendra; Singh, Veena; Mehta, Dalip Singh

2016-04-01

In this Letter, we demonstrate quantitative phase imaging of biological samples, such as human red blood cells (RBCs) and onion cells using narrow temporal frequency and wide angular frequency spectrum light source. This type of light source was synthesized by the combined effect of spatial, angular, and temporal diversity of speckle reduction technique. The importance of using low spatial and high temporal coherence light source over the broad band and narrow band light source is that it does not require any dispersion compensation mechanism for biological samples. Further, it avoids the formation of speckle or spurious fringes which arises while using narrow band light source.

Omics/systems biology and cancer cachexia.

PubMed

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

2016-06-01

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

GPS for QSP: A Summary of the ACoP6 Symposium on Quantitative Systems Pharmacology and a Stage for Near‐Term Efforts in the Field

PubMed Central

Musante, CJ; Abernethy, DR; Allerheiligen, SR; Lauffenburger, DA

2016-01-01

Quantitative Systems Pharmacology (QSP) is experiencing increased application in the drug discovery and development process. Like its older sibling, systems biology, the QSP field is comprised of a mix of established disciplines and methods, from molecular biology to engineering to pharmacometrics.1 As a result, there exist critical segments of the discipline that differ dramatically in approach and a need to bring these groups together toward a common goal. PMID:27639191

All biology is computational biology.

PubMed

Markowetz, Florian

2017-03-01

Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.

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

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.

Mass spectrometry in systems biology an introduction.

PubMed

Dunn, Warwick B

2011-01-01

The qualitative detection, quantification, and structural characterization of analytes in biological systems are important requirements for objectives to be fulfilled in systems biology research. One analytical tool applied to a multitude of systems biology studies is mass spectrometry, particularly for the study of proteins and metabolites. Here, the role of mass spectrometry in systems biology will be assessed, the advantages and disadvantages discussed, and the instrument configurations available described. Finally, general applications will be briefly reviewed. Copyright © 2011 Elsevier Inc. All rights reserved.

The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics

PubMed Central

Corwin, Jason A.; Copeland, Daniel; Feusier, Julie; Subedy, Anushriya; Eshbaugh, Robert; Palmer, Christine; Maloof, Julin; Kliebenstein, Daniel J.

2016-01-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes. PMID:26866607

The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics.

PubMed

Corwin, Jason A; Copeland, Daniel; Feusier, Julie; Subedy, Anushriya; Eshbaugh, Robert; Palmer, Christine; Maloof, Julin; Kliebenstein, Daniel J

2016-02-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes.

A Converter from the Systems Biology Markup Language to the Synthetic Biology Open Language.

PubMed

Nguyen, Tramy; Roehner, Nicholas; Zundel, Zach; Myers, Chris J

2016-06-17

Standards are important to synthetic biology because they enable exchange and reproducibility of genetic designs. This paper describes a procedure for converting between two standards: the Systems Biology Markup Language (SBML) and the Synthetic Biology Open Language (SBOL). SBML is a standard for behavioral models of biological systems at the molecular level. SBOL describes structural and basic qualitative behavioral aspects of a biological design. Converting SBML to SBOL enables a consistent connection between behavioral and structural information for a biological design. The conversion process described in this paper leverages Systems Biology Ontology (SBO) annotations to enable inference of a designs qualitative function.

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.

Coupling biology and oceanography in models.

PubMed

Fennel, W; Neumann, T

2001-08-01

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

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.

Radiation interactions with biological systems.

PubMed

Islam, Muhammad Torequl

2017-05-01

The use of radiation, especially ionizing radiation (IR), is currently attracting great attention in the field of medical sciences. However, it should be mentioned that IR has both beneficial and harmful effects in biological systems. This review aims to focus on IR-mediated physiological events in a mechanistic way. Evidence from the databases, mainly from PUBMED and SCIENCE DIRECT were considered. IR directly and/or with their lyses products (indirect) causes oxidative stresses to biological systems. These activities may be localized and systematic. Otherwise, IR-induced non-/multi-targeted effects are also evident. IR in diagnosis and cancer radiotherapy is well-known. Reactive species produced by IR are not only beneficial, but also can exert harmful effects in a biological system such as aging, genetic instability and mutagenicity, membrane lysis and cell death, alteration of enzymatic activity and metabolic events, mitochondrial dysfunction, and even cancer. Additionally, DNA adducts formation, after IR-induced DNA breakage, is a cause of blockage of DNA repair capability with an increase in cellular radiosensitivity. These may allow cellular ruin even at low IR levels. Dependent on the dose, duration of action and quality, IR plays diverse roles in biological systems.

A feeling for the numbers in biology

PubMed Central

Phillips, Rob; Milo, Ron

2009-01-01

Although the quantitative description of biological systems has been going on for centuries, recent advances in the measurement of phenomena ranging from metabolism to gene expression to signal transduction have resulted in a new emphasis on biological numeracy. This article describes the confluence of two different approaches to biological numbers. First, an impressive array of quantitative measurements make it possible to develop intuition about biological numbers ranging from how many gigatons of atmospheric carbon are fixed every year in the process of photosynthesis to the number of membrane transporters needed to provide sugars to rapidly dividing Escherichia coli cells. As a result of the vast array of such quantitative data, the BioNumbers web site has recently been developed as a repository for biology by the numbers. Second, a complementary and powerful tradition of numerical estimates familiar from the physical sciences and canonized in the so-called “Fermi problems” calls for efforts to estimate key biological quantities on the basis of a few foundational facts and simple ideas from physics and chemistry. In this article, we describe these two approaches and illustrate their synergism in several particularly appealing case studies. These case studies reveal the impact that an emphasis on numbers can have on important biological questions. PMID:20018695

Quantitative proteomics in Giardia duodenalis-Achievements and challenges.

PubMed

Emery, Samantha J; Lacey, Ernest; Haynes, Paul A

2016-08-01

Giardia duodenalis (syn. G. lamblia and G. intestinalis) is a protozoan parasite of vertebrates and a major contributor to the global burden of diarrheal diseases and gastroenteritis. The publication of multiple genome sequences in the G. duodenalis species complex has provided important insights into parasite biology, and made post-genomic technologies, including proteomics, significantly more accessible. The aims of proteomics are to identify and quantify proteins present in a cell, and assign functions to them within the context of dynamic biological systems. In Giardia, proteomics in the post-genomic era has transitioned from reliance on gel-based systems to utilisation of a diverse array of techniques based on bottom-up LC-MS/MS technologies. Together, these have generated crucial foundations for subcellular proteomes, elucidated intra- and inter-assemblage isolate variation, and identified pathways and markers in differentiation, host-parasite interactions and drug resistance. However, in Giardia, proteomics remains an emerging field, with considerable shortcomings evident from the published research. These include a bias towards assemblage A, a lack of emphasis on quantitative analytical techniques, and limited information on post-translational protein modifications. Additionally, there are multiple areas of research for which proteomic data is not available to add value to published transcriptomic data. The challenge of amalgamating data in the systems biology paradigm necessitates the further generation of large, high-quality quantitative datasets to accurately model parasite biology. This review surveys the current proteomic research available for Giardia and evaluates their technical and quantitative approaches, while contextualising their biological insights into parasite pathology, isolate variation and eukaryotic evolution. Finally, we propose areas of priority for the generation of future proteomic data to explore fundamental questions in Giardia

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Hoops, Stefan; Keating, Sarah M.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528564

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

Quantitative fluorescence and elastic scattering tissue polarimetry using an Eigenvalue calibrated spectroscopic Mueller matrix system.

PubMed

Soni, Jalpa; Purwar, Harsh; Lakhotia, Harshit; Chandel, Shubham; Banerjee, Chitram; Kumar, Uday; Ghosh, Nirmalya

2013-07-01

A novel spectroscopic Mueller matrix system has been developed and explored for both fluorescence and elastic scattering polarimetric measurements from biological tissues. The 4 × 4 Mueller matrix measurement strategy is based on sixteen spectrally resolved (λ = 400 - 800 nm) measurements performed by sequentially generating and analyzing four elliptical polarization states. Eigenvalue calibration of the system ensured high accuracy of Mueller matrix measurement over a broad wavelength range, either for forward or backscattering geometry. The system was explored for quantitative fluorescence and elastic scattering spectroscopic polarimetric studies on normal and precancerous tissue sections from human uterine cervix. The fluorescence spectroscopic Mueller matrices yielded an interesting diattenuation parameter, exhibiting differences between normal and precancerous tissues.

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.

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.

Measurement issues associated with quantitative molecular biology analysis of complex food matrices for the detection of food fraud.

PubMed

Burns, Malcolm; Wiseman, Gordon; Knight, Angus; Bramley, Peter; Foster, Lucy; Rollinson, Sophie; Damant, Andrew; Primrose, Sandy

2016-01-07

Following a report on a significant amount of horse DNA being detected in a beef burger product on sale to the public at a UK supermarket in early 2013, the Elliott report was published in 2014 and contained a list of recommendations for helping ensure food integrity. One of the recommendations included improving laboratory testing capacity and capability to ensure a harmonised approach for testing for food authenticity. Molecular biologists have developed exquisitely sensitive methods based on the polymerase chain reaction (PCR) or mass spectrometry for detecting the presence of particular nucleic acid or peptide/protein sequences. These methods have been shown to be specific and sensitive in terms of lower limits of applicability, but they are largely qualitative in nature. Historically, the conversion of these qualitative techniques into reliable quantitative methods has been beset with problems even when used on relatively simple sample matrices. When the methods are applied to complex sample matrices, as found in many foods, the problems are magnified resulting in a high measurement uncertainty associated with the result which may mean that the assay is not fit for purpose. However, recent advances in the technology and the understanding of molecular biology approaches have further given rise to the re-assessment of these methods for their quantitative potential. This review focuses on important issues for consideration when validating a molecular biology assay and the various factors that can impact on the measurement uncertainty of a result associated with molecular biology approaches used in detection of food fraud, with a particular focus on quantitative PCR-based and proteomics assays.

Human genomic DNA quantitation system, H-Quant: development and validation for use in forensic casework.

PubMed

Shewale, Jaiprakash G; Schneida, Elaine; Wilson, Jonathan; Walker, Jerilyn A; Batzer, Mark A; Sinha, Sudhir K

2007-03-01

The human DNA quantification (H-Quant) system, developed for use in human identification, enables quantitation of human genomic DNA in biological samples. The assay is based on real-time amplification of AluYb8 insertions in hominoid primates. The relatively high copy number of subfamily-specific Alu repeats in the human genome enables quantification of very small amounts of human DNA. The oligonucleotide primers present in H-Quant are specific for human DNA and closely related great apes. During the real-time PCR, the SYBR Green I dye binds to the DNA that is synthesized by the human-specific AluYb8 oligonucleotide primers. The fluorescence of the bound SYBR Green I dye is measured at the end of each PCR cycle. The cycle at which the fluorescence crosses the chosen threshold correlates to the quantity of amplifiable DNA in that sample. The minimal sensitivity of the H-Quant system is 7.6 pg/microL of human DNA. The amplicon generated in the H-Quant assay is 216 bp, which is within the same range of the common amplifiable short tandem repeat (STR) amplicons. This size amplicon enables quantitation of amplifiable DNA as opposed to a quantitation of degraded or nonamplifiable DNA of smaller sizes. Development and validation studies were performed on the 7500 real-time PCR system following the Quality Assurance Standards for Forensic DNA Testing Laboratories.

Quantitative reactive modeling and verification.

PubMed

Henzinger, Thomas A

Formal verification aims to improve the quality of software by detecting errors before they do harm. At the basis of formal verification is the logical notion of correctness , which purports to capture whether or not a program behaves as desired. We suggest that the boolean partition of software into correct and incorrect programs falls short of the practical need to assess the behavior of software in a more nuanced fashion against multiple criteria. We therefore propose to introduce quantitative fitness measures for programs, specifically for measuring the function, performance, and robustness of reactive programs such as concurrent processes. This article describes the goals of the ERC Advanced Investigator Project QUAREM. The project aims to build and evaluate a theory of quantitative fitness measures for reactive models. Such a theory must strive to obtain quantitative generalizations of the paradigms that have been success stories in qualitative reactive modeling, such as compositionality, property-preserving abstraction and abstraction refinement, model checking, and synthesis. The theory will be evaluated not only in the context of software and hardware engineering, but also in the context of systems biology. In particular, we will use the quantitative reactive models and fitness measures developed in this project for testing hypotheses about the mechanisms behind data from biological experiments.

Method of measurement in biological systems

DOEpatents

Turteltaub, K.W.; Vogel, J.S.; Felton, J.S.; Gledhill, B.L.; Davis, J.C.

1994-12-27

Disclosed is a method of quantifying molecules in biological substances comprising: a. selecting a biological host in which radioisotopes are present in concentrations equal to or less than those in the ambient biosphere, b. preparing a long-lived radioisotope labeled reactive chemical specie, c. administering the chemical specie to the biological host in doses sufficiently low to avoid significant overt damage to the biological system, d. allowing a period of time to elapse sufficient for dissemination and interaction of the chemical specie with the host throughout the biological system of the host, e. isolating a reacted fraction of the biological substance from the host in a manner sufficient to avoid contamination of the substance from extraneous sources, f. converting the fraction of biological substance by suitable means to a material which efficiently produces charged ions in at least one of several possible ion sources without introduction of significant isotopic fractionation, and, g. measuring the radioisotope concentration in the material by means of direct isotopic counting. 5 figures.

Method of measurement in biological systems

DOEpatents

Turteltaub, K.W.; Vogel, J.S.; Felton, J.S.; Gledhill, B.L.: Davis, J.C.; Stanker, L.H.

1993-05-11

A method is disclosed of quantifying molecules in biological substances, comprising: selecting a biological host in which radioisotopes are present in concentrations equal to or less than those in the ambient biosphere; preparing a long-lived radioisotope labeled reactive chemical specie; administering the chemical specie to the biological host in doses sufficiently low to avoid significant overt damage to the biological system; allowing a period of time to elapse sufficient for dissemination and interaction of the chemical specie with the host throughout the biological system of the host; isolating a reacted fraction of the biological substance from the host in a manner sufficient to avoid contamination of the substance from extraneous sources; converting the fraction of biological substance by suitable means to a material which efficiently produces charged ions in at least one of several possible ion sources without introduction of significant isotopic fractionation; and measuring the radioisotope concentration in the material by means of direct isotopic counting.

Method of measurement in biological systems

DOEpatents

Turteltaub, Kenneth W.; Vogel, John S.; Felton, James S.; Gledhill, Barton L.; Davis, Jay C.; Stanker, Larry H.

1993-05-11

Disclosed is a method of quantifying molecules in biological substances, comprising: a. selecting a biological host in which radioisotopes are present in concentrations equal to or less than those in the ambient biosphere, b. preparing a long-lived radioisotope labeled reactive chemical specie, c. administering said chemical specie to said biological host in doses sufficiently low to avoid significant overt damage to the biological system thereof, d. allowing a period of time to elapse sufficient for dissemination and interaction of said chemical specie with said host throughout said biological system of said host, e. isolating a reacted fraction of the biological substance from said host in a manner sufficient to avoid contamination of said substance from extraneous sources, f. converting said fraction of biological substance by suitable means to a material which efficiently produces charged ions in at least one of several possible ion sources without introduction of significant isotopic fractionation, and, g. measuring the radioisotope concentration in said material by means of direct isotopic counting.

Method of measurement in biological systems

DOEpatents

Turteltaub, Kenneth W.; Vogel, John S.; Felton, James S.; Gledhill, Barton L.; Davis, Jay C.

1994-01-01

Disclosed is a method of quantifying molecules in biological substances comprising: a. selecting a biological host in which radioisotopes are present in concentrations equal to or less than those in the ambient biosphere, b. preparing a long-lived radioisotope labeled reactive chemical specie, c. administering said chemical specie to said biological host in doses sufficiently low to avoid significant overt damage to the biological system thereof, d. allowing a period of time to elapse sufficient for dissemination and interaction of said chemical specie with said host throughout said biological system of said host, e. isolating a reacted fraction of the biological substance from said host in a manner sufficient to avoid contamination of said substance from extraneous sources, f. converting said fraction of biological substance by suitable means to a material which efficiently produces charged ions in at least one of several possible ion sources without introduction of significant isotopic fractionation, and, g. measuring the radioisotope concentration in said material by means of direct isotopic counting.

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

[Methods of quantitative proteomics].

PubMed

Kopylov, A T; Zgoda, V G

2007-01-01

In modern science proteomic analysis is inseparable from other fields of systemic biology. Possessing huge resources quantitative proteomics operates colossal information on molecular mechanisms of life. Advances in proteomics help researchers to solve complex problems of cell signaling, posttranslational modification, structure and functional homology of proteins, molecular diagnostics etc. More than 40 various methods have been developed in proteomics for quantitative analysis of proteins. Although each method is unique and has certain advantages and disadvantages all these use various isotope labels (tags). In this review we will consider the most popular and effective methods employing both chemical modifications of proteins and also metabolic and enzymatic methods of isotope labeling.

A system for success: BMC Systems Biology, a new open access journal.

PubMed

Hodgkinson, Matt J; Webb, Penelope A

2007-09-04

BMC Systems Biology is the first open access journal spanning the growing field of systems biology from molecules up to ecosystems. The journal has launched as more and more institutes are founded that are similarly dedicated to this new approach. BMC Systems Biology builds on the ongoing success of the BMC series, providing a venue for all sound research in the systems-level analysis of biology.

Hierarchical structure of biological systems: a bioengineering approach.

PubMed

Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

2014-01-01

A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems.

GPS for QSP: A Summary of the ACoP6 Symposium on Quantitative Systems Pharmacology and a Stage for Near-Term Efforts in the Field.

PubMed

Musante, C J; Abernethy, D R; Allerheiligen, S R; Lauffenburger, D A; Zager, M G

2016-09-01

Quantitative Systems Pharmacology (QSP) is experiencing increased application in the drug discovery and development process. Like its older sibling, systems biology, the QSP field is comprised of a mix of established disciplines and methods, from molecular biology to engineering to pharmacometrics. As a result, there exist critical segments of the discipline that differ dramatically in approach and a need to bring these groups together toward a common goal. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Conceptual Foundations of Systems Biology Explaining Complex Cardiac Diseases.

PubMed

Louridas, George E; Lourida, Katerina G

2017-02-21

Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine. Especially, in the field of cardiology, these concepts can be used to explain complex clinical cardiac phenotypes like chronic heart failure and coronary artery disease. Cardiac diseases are biological complex entities which like other biological phenomena can be explained by a systems biology approach. The above powerful biological tools of systems biology can explain robustness growth and stability during disease process from modulation to phenotype. The purpose of the present review paper is to implement systems biology strategy and incorporate some conceptual issues raised by this approach into the clinical field of complex cardiac diseases. Cardiac disease process and progression can be addressed by the holistic realistic approach of systems biology in order to define in better terms earlier diagnosis and more effective therapy.

Applicability of computational systems biology in toxicology.

PubMed

Kongsbak, Kristine; Hadrup, Niels; Audouze, Karine; Vinggaard, Anne Marie

2014-07-01

Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research. © 2014 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

The Feasibility of Systems Thinking in Biology Education

ERIC Educational Resources Information Center

Boersma, Kerst; Waarlo, Arend Jan; Klaassen, Kees

2011-01-01

Systems thinking in biology education is an up and coming research topic, as yet with contrasting feasibility claims. In biology education systems thinking can be understood as thinking backward and forward between concrete biological objects and processes and systems models representing systems theoretical characteristics. Some studies claim that…

New Tools and New Biology: Recent Miniaturized Systems for Molecular and Cellular Biology

PubMed Central

Hamon, Morgan; Hong, Jong Wook

2013-01-01

Recent advances in applied physics and chemistry have led to the development of novel microfluidic systems. Microfluidic systems allow minute amounts of reagents to be processed using μm-scale channels and offer several advantages over conventional analytical devices for use in biological sciences: faster, more accurate and more reproducible analytical performance, reduced cell and reagent consumption, portability, and integration of functional components in a single chip. In this review, we introduce how microfluidics has been applied to biological sciences. We first present an overview of the fabrication of microfluidic systems and describe the distinct technologies available for biological research. We then present examples of microsystems used in biological sciences, focusing on applications in molecular and cellular biology. PMID:24305843

The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization.

PubMed

West, Geoffrey B; Brown, James H

2005-05-01

Life is the most complex physical phenomenon in the Universe, manifesting an extraordinary diversity of form and function over an enormous scale from the largest animals and plants to the smallest microbes and subcellular units. Despite this many of its most fundamental and complex phenomena scale with size in a surprisingly simple fashion. For example, metabolic rate scales as the 3/4-power of mass over 27 orders of magnitude, from molecular and intracellular levels up to the largest organisms. Similarly, time-scales (such as lifespans and growth rates) and sizes (such as bacterial genome lengths, tree heights and mitochondrial densities) scale with exponents that are typically simple powers of 1/4. The universality and simplicity of these relationships suggest that fundamental universal principles underly much of the coarse-grained generic structure and organisation of living systems. We have proposed a set of principles based on the observation that almost all life is sustained by hierarchical branching networks, which we assume have invariant terminal units, are space-filling and are optimised by the process of natural selection. We show how these general constraints explain quarter power scaling and lead to a quantitative, predictive theory that captures many of the essential features of diverse biological systems. Examples considered include animal circulatory systems, plant vascular systems, growth, mitochondrial densities, and the concept of a universal molecular clock. Temperature considerations, dimensionality and the role of invariants are discussed. Criticisms and controversies associated with this approach are also addressed.

Interactomes, manufacturomes and relational biology: analogies between systems biology and manufacturing systems

PubMed Central

2011-01-01

Background We review and extend the work of Rosen and Casti who discuss category theory with regards to systems biology and manufacturing systems, respectively. Results We describe anticipatory systems, or long-range feed-forward chemical reaction chains, and compare them to open-loop manufacturing processes. We then close the loop by discussing metabolism-repair systems and describe the rationality of the self-referential equation f = f (f). This relationship is derived from some boundary conditions that, in molecular systems biology, can be stated as the cardinality of the following molecular sets must be about equal: metabolome, genome, proteome. We show that this conjecture is not likely correct so the problem of self-referential mappings for describing the boundary between living and nonliving systems remains an open question. We calculate a lower and upper bound for the number of edges in the molecular interaction network (the interactome) for two cellular organisms and for two manufacturomes for CMOS integrated circuit manufacturing. Conclusions We show that the relevant mapping relations may not be Abelian, and that these problems cannot yet be resolved because the interactomes and manufacturomes are incomplete. PMID:21689427

Positioning Genomics in Biology Education: Content Mapping of Undergraduate Biology Textbooks†

PubMed Central

Wernick, Naomi L. B.; Ndung’u, Eric; Haughton, Dominique; Ledley, Fred D.

2014-01-01

Biological thought increasingly recognizes the centrality of the genome in constituting and regulating processes ranging from cellular systems to ecology and evolution. In this paper, we ask whether genomics is similarly positioned as a core concept in the instructional sequence for undergraduate biology. Using quantitative methods, we analyzed the order in which core biological concepts were introduced in textbooks for first-year general and human biology. Statistical analysis was performed using self-organizing map algorithms and conventional methods to identify clusters of terms and their relative position in the books. General biology textbooks for both majors and nonmajors introduced genome-related content after text related to cell biology and biological chemistry, but before content describing higher-order biological processes. However, human biology textbooks most often introduced genomic content near the end of the books. These results suggest that genomics is not yet positioned as a core concept in commonly used textbooks for first-year biology and raises questions about whether such textbooks, or courses based on the outline of these textbooks, provide an appropriate foundation for understanding contemporary biological science. PMID:25574293

Positioning genomics in biology education: content mapping of undergraduate biology textbooks.

PubMed

Wernick, Naomi L B; Ndung'u, Eric; Haughton, Dominique; Ledley, Fred D

2014-12-01

Biological thought increasingly recognizes the centrality of the genome in constituting and regulating processes ranging from cellular systems to ecology and evolution. In this paper, we ask whether genomics is similarly positioned as a core concept in the instructional sequence for undergraduate biology. Using quantitative methods, we analyzed the order in which core biological concepts were introduced in textbooks for first-year general and human biology. Statistical analysis was performed using self-organizing map algorithms and conventional methods to identify clusters of terms and their relative position in the books. General biology textbooks for both majors and nonmajors introduced genome-related content after text related to cell biology and biological chemistry, but before content describing higher-order biological processes. However, human biology textbooks most often introduced genomic content near the end of the books. These results suggest that genomics is not yet positioned as a core concept in commonly used textbooks for first-year biology and raises questions about whether such textbooks, or courses based on the outline of these textbooks, provide an appropriate foundation for understanding contemporary biological science.

Systems Biology Analysis of Heterocellular Signaling.

PubMed

Tape, Christopher J

2016-08-01

Tissues comprise multiple heterotypic cell types (e.g., epithelial, mesenchymal, and immune cells). Communication between heterotypic cell types is essential for biological cohesion and is frequently dysregulated in disease. Despite the importance of heterocellular communication, most systems biology techniques do not report cell-specific signaling data from mixtures of cells. As a result, our existing perspective of cellular behavior under-represents the influence of heterocellular signaling. Recent technical advances now permit the resolution of systems-level cell-specific signaling data. This review discusses how new physical, spatial, and isotopic resolving methods are facilitating unique systems biology studies of heterocellular communication. Copyright © 2016 Elsevier Ltd. All rights reserved.

Consistent design schematics for biological systems: standardization of representation in biological engineering

PubMed Central

Matsuoka, Yukiko; Ghosh, Samik; Kitano, Hiroaki

2009-01-01

The discovery by design paradigm driving research in synthetic biology entails the engineering of de novo biological constructs with well-characterized input–output behaviours and interfaces. The construction of biological circuits requires iterative phases of design, simulation and assembly, leading to the fabrication of a biological device. In order to represent engineered models in a consistent visual format and further simulating them in silico, standardization of representation and model formalism is imperative. In this article, we review different efforts for standardization, particularly standards for graphical visualization and simulation/annotation schemata adopted in systems biology. We identify the importance of integrating the different standardization efforts and provide insights into potential avenues for developing a common framework for model visualization, simulation and sharing across various tools. We envision that such a synergistic approach would lead to the development of global, standardized schemata in biology, empowering deeper understanding of molecular mechanisms as well as engineering of novel biological systems. PMID:19493898

Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2018-03-01

I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

Quantitative dispersion microscopy

PubMed Central

Fu, Dan; Choi, Wonshik; Sung, Yongjin; Yaqoob, Zahid; Dasari, Ramachandra R.; Feld, Michael

2010-01-01

Refractive index dispersion is an intrinsic optical property and a useful source of contrast in biological imaging studies. In this report, we present the first dispersion phase imaging of living eukaryotic cells. We have developed quantitative dispersion microscopy based on the principle of quantitative phase microscopy. The dual-wavelength quantitative phase microscope makes phase measurements at 310 nm and 400 nm wavelengths to quantify dispersion (refractive index increment ratio) of live cells. The measured dispersion of living HeLa cells is found to be around 1.088, which agrees well with that measured directly for protein solutions using total internal reflection. This technique, together with the dry mass and morphology measurements provided by quantitative phase microscopy, could prove to be a useful tool for distinguishing different types of biomaterials and studying spatial inhomogeneities of biological samples. PMID:21113234

Correlative Instrumental Neutron Activation Analysis, Light Microscopy, Transmission Electron Microscopy, and X-ray Microanalysis for Qualitative and Quantitative Detection of Colloidal Gold Spheres in Biological Specimens

NASA Astrophysics Data System (ADS)

Hillyer, Julián F.; Albrecht, Ralph M.

1998-10-01

: Colloidal gold, conjugated to ligands or antibodies, is routinely used as a label for the detection of cell structures by light (LM) and electron microscopy (EM). To date, several methods to count the number of colloidal gold labels have been employed with limited success. Instrumental neutron activation analysis (INAA), a physical method for the analysis of the elemental composition of materials, can be used to provide a quantitative index of gold accumulation in bulk specimens. Given that gold is not naturally found in biological specimens in any substantial amount and that colloidal gold and ligand conjugates can be prepared to yield uniform bead sizes, the amount of label can be calculated in bulk biological samples by INAA. Here we describe the use of INAA, LM, transmission EM, and X-ray microanalysis (EDX) in a model to determine both distribution (localization) and amount of colloidal gold at the organ, tissue, cellular, and ultrastructural levels in whole animal systems following administration. In addition, the sensitivity for gold in biological specimens by INAA is compared with that of inductively coupled plasma mass spectrometry (ICP-MS). The correlative use of INAA, LM, TEM, and EDX can be useful, for example, in the quantitative and qualitative tracking of various labeled molecular species following administration in vivo.

Analysis of arsenical metabolites in biological samples.

PubMed

Hernandez-Zavala, Araceli; Drobna, Zuzana; Styblo, Miroslav; Thomas, David J

2009-11-01

Quantitation of iAs and its methylated metabolites in biological samples provides dosimetric information needed to understand dose-response relations. Here, methods are described for separation of inorganic and mono-, di-, and trimethylated arsenicals by thin layer chromatography. This method has been extensively used to track the metabolism of the radionuclide [(73)As] in a variety of in vitro assay systems. In addition, a hydride generation-cryotrapping-gas chromatography-atomic absorption spectrometric method is described for the quantitation of arsenicals in biological samples. This method uses pH-selective hydride generation to differentiate among arsenicals containing trivalent or pentavalent arsenic.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach

PubMed Central

Laurent, Georges St.; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J.L.; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R.R.; Nicolas, Estelle; McCaffrey, Timothy A.; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

2016-01-01

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlincRNAs genes likely function in cis to activate nearby genes. This effect while most pronounced in closely spaced vlincRNA–gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlincRNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. PMID:27001520

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M.; Le Novére, Nicolas; Myers, Chris J.; Olivier, Brett G.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Waltemath, Dagmar; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528569

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

Biologically inspired dynamic material systems.

PubMed

Studart, André R

2015-03-09

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

An integrative strategy for quantitative analysis of the N-glycoproteome in complex biological samples.

PubMed

Wang, Ji; Zhou, Chuang; Zhang, Wei; Yao, Jun; Lu, Haojie; Dong, Qiongzhu; Zhou, Haijun; Qin, Lunxiu

2014-01-15

The complexity of protein glycosylation makes it difficult to characterize glycosylation patterns on a proteomic scale. In this study, we developed an integrated strategy for comparatively analyzing N-glycosylation/glycoproteins quantitatively from complex biological samples in a high-throughput manner. This strategy entailed separating and enriching glycopeptides/glycoproteins using lectin affinity chromatography, and then tandem labeling them with 18O/16O to generate a mass shift of 6 Da between the paired glycopeptides, and finally analyzing them with liquid chromatography-mass spectrometry (LC-MS) and the automatic quantitative method we developed based on Mascot Distiller. The accuracy and repeatability of this strategy were first verified using standard glycoproteins; linearity was maintained within a range of 1:10-10:1. The peptide concentration ratios obtained by the self-build quantitative method were similar to both the manually calculated and theoretical values, with a standard deviation (SD) of 0.023-0.186 for glycopeptides. The feasibility of the strategy was further confirmed with serum from hepatocellular carcinoma (HCC) patients and healthy individuals; the expression of 44 glycopeptides and 30 glycoproteins were significantly different between HCC patient and control serum. This strategy is accurate, repeatable, and efficient, and may be a useful tool for identification of disease-related N-glycosylation/glycoprotein changes.

Metabolomics: Definitions and Significance in Systems Biology.

PubMed

Klassen, Aline; Faccio, Andréa Tedesco; Canuto, Gisele André Baptista; da Cruz, Pedro Luis Rocha; Ribeiro, Henrique Caracho; Tavares, Marina Franco Maggi; Sussulini, Alessandra

2017-01-01

Nowadays, there is a growing interest in deeply understanding biological mechanisms not only at the molecular level (biological components) but also the effects of an ongoing biological process in the organism as a whole (biological functionality), as established by the concept of systems biology. Within this context, metabolomics is one of the most powerful bioanalytical strategies that allow obtaining a picture of the metabolites of an organism in the course of a biological process, being considered as a phenotyping tool. Briefly, metabolomics approach consists in identifying and determining the set of metabolites (or specific metabolites) in biological samples (tissues, cells, fluids, or organisms) under normal conditions in comparison with altered states promoted by disease, drug treatment, dietary intervention, or environmental modulation. The aim of this chapter is to review the fundamentals and definitions used in the metabolomics field, as well as to emphasize its importance in systems biology and clinical studies.

Systems biology for organotypic cell cultures.

PubMed

Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

2017-01-01

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

Quantitative Predictive Models for Systemic Toxicity (SOT)

EPA Science Inventory

Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

Stochastic simulation in systems biology

PubMed Central

Székely, Tamás; Burrage, Kevin

2014-01-01

Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity). In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology. There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest. PMID:25505503

Systems biology: the case for a systems science approach to diabetes.

PubMed

Petrasek, Danny

2008-01-01

The unprecedented accumulation of biological data in recent decades has underscored the need to organize and integrate the massive collection of information. In addition, there is rising agreement among biologists that a complete understanding of a single cell will not lead directly to a complete understanding of a system of cells. The success of a systems science approach in engineering and physics may be of great value in the evolution of biological science. This article reviews some examples that suggest the importance of a systems biology approach and, in addition, advance one specific systems science principle, the conservation of uncertainty, which may give insight into the emergent behavior of numerous biological and physiological phenomena.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2018-03-09

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.

Exhaust-System Leak Test : Quantitative Procedure

DOT National Transportation Integrated Search

1974-01-01

A quantitative, periodic motor vehicle safety-inspection test for determining the leakage rate of engine exhaust from an automotive exhaust system was investigated. Two technical approaches were evaluated, and the better one was selected for developm...

Mathematical and Computational Modeling in Complex Biological Systems

PubMed Central

Li, Wenyang; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. PMID:28386558

Mathematical and Computational Modeling in Complex Biological Systems.

PubMed

Ji, Zhiwei; Yan, Ke; Li, Wenyang; Hu, Haigen; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

Systems Biology of Industrial Microorganisms

NASA Astrophysics Data System (ADS)

Papini, Marta; Salazar, Margarita; Nielsen, Jens

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

Systems biology of industrial microorganisms.

PubMed

Papini, Marta; Salazar, Margarita; Nielsen, Jens

2010-01-01

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

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.

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

Systems Biology for Organotypic Cell Cultures

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

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the

CINRG: Systems Biology of Glucocorticoids in Muscle Disease

DTIC Science & Technology

2012-10-01

Duchenne Muscular dystrophy , Glucocorticoids, Systems biology, Drug mechanism CINRG: Systems Biology of Glucocorticoids in Muscle Disease Zuyi Wang, Ph.D...2011-2012) for Contract W81XWH-09-1-0726 SYSTEMS BIOLOGY OF GLUCOCORTICOIDS IN MUSCLE DISEASE Introduction Duchenne muscular dystrophy ...DMD) is the most common and incurable muscular dystrophy of childhood. Muscle regeneration fails with advancing age, leading to considerable fibrosis

Natural production of biological optical systems

NASA Astrophysics Data System (ADS)

Choi, Seung Ho; Kim, Young L.

2015-03-01

Synthesis and production in nature often provide ideas to design and fabricate advanced biomimetic photonic materials and structures, leading to excellent physical properties and enhanced performance. In addition, the recognition and utilization of natural or biological substances have been typical routes to develop biocompatible and biodegradable materials for medical applications. In this respect, biological lasers utilizing such biomaterials and biostructures have been received considerable attention, given a variety of implications and potentials for bioimaging, biosensing, implantation, and therapy. However, without relying on industrial facilities, eco-friendly massive production of such optical components or systems has not yet been investigated. We show examples of bioproduction of biological lasers using agriculture and fisheries. We anticipate that such approaches will open new possibilities for scalable eco-friendly `green' production of biological photonics components and systems.

Gender, Math Confidence, and Grit: Relationships with Quantitative Skills and Performance in an Undergraduate Biology Course

PubMed Central

Flanagan, K. M.; Einarson, J.

2017-01-01

In a world filled with big data, mathematical models, and statistics, the development of strong quantitative skills is becoming increasingly critical for modern biologists. Teachers in this field must understand how students acquire quantitative skills and explore barriers experienced by students when developing these skills. In this study, we examine the interrelationships among gender, grit, and math confidence for student performance on a pre–post quantitative skills assessment and overall performance in an undergraduate biology course. Here, we show that females significantly underperformed relative to males on a quantitative skills assessment at the start of term. However, females showed significantly higher gains over the semester, such that the gender gap in performance was nearly eliminated by the end of the semester. Math confidence plays an important role in the performance on both the pre and post quantitative skills assessments and overall performance in the course. The effect of grit on student performance, however, is mediated by a student’s math confidence; as math confidence increases, the positive effect of grit decreases. Consequently, the positive impact of a student’s grittiness is observed most strongly for those students with low math confidence. We also found grit to be positively associated with the midterm score and the final grade in the course. Given the relationships established in this study among gender, grit, and math confidence, we provide “instructor actions” from the literature that can be applied in the classroom to promote the development of quantitative skills in light of our findings. PMID:28798209

Exploring Phytoplankton Population Investigation Growth to Enhance Quantitative Literacy

ERIC Educational Resources Information Center

Baumgartner, Erin; Biga, Lindsay; Bledsoe, Karen; Dawson, James; Grammer, Julie; Howard, Ava; Snyder, Jeffrey

2015-01-01

Quantitative literacy is essential to biological literacy (and is one of the core concepts in "Vision and Change in Undergraduate Biology Education: A Call to Action"; AAAS 2009). Building quantitative literacy is a challenging endeavor for biology instructors. Integrating mathematical skills into biological investigations can help build…

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

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

PubMed

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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

Quantitative Diagnosis of Continuous-Valued, Stead-State Systems

NASA Technical Reports Server (NTRS)

Rouquette, N.

1995-01-01

Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.

Predicting lymph node output efficiency using systems biology

PubMed Central

Gong, Chang; Mattila, Joshua T.; Miller, Mark; Flynn, JoAnne L.; Linderman, Jennifer J.; Kirschner, D.

2013-01-01

Dendritic cells (DCs) capture pathogens and foreign antigen (Ag) in peripheral tissues and migrate to secondary lymphoid tissues, such as lymph nodes (LNs), where they present processed Ag as MHC-bound peptide (pMHC) to naïve T cells. Interactions between DCs and T cells result, over periods of hours, in activation, clonal expansion and differentiation of antigen-specific T cells, leading to primed cells that can now participate in immune responses. Two-photon microscopy (2PM) has been widely adopted to analyze lymphocyte dynamics and can serve as a powerful in vivo assay for cell trafficking and activation over short length and time scales. Linking biological phenomena between vastly different spatiotemporal scales can be achieved using a systems biology approach. We developed a 3D agent-based cellular model of a LN that allows for the simultaneous in silico simulation of T cell trafficking, activation and production of effector cells under different antigen (Ag) conditions. The model anatomy is based on in situ analysis of LN sections (from primates and mice) and cell dynamics based on quantitative measurements from 2PM imaging of mice. Our simulations make three important predictions. First, T cell encounters by DCs and T cell receptor (TCR) repertoire scanning are more efficient in a 3D model compared with 2D, suggesting that a 3D model is needed to analyze LN function. Second, LNs are able to produce primed CD4+T cells at the same efficiency over broad ranges of cognate frequencies (from 10−5 to 10−2). Third, reducing the time that naïve T cells are required to bind DCs before becoming activated will increase the rate at which effector cells are produced. This 3D model provides a robust platform to study how T cell trafficking and activation dynamics relate to the efficiency of T cell priming and clonal expansion. We envision that this systems biology approach will provide novel insights for guiding vaccine development and understanding immune responses

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.

An Augmented γ-Spray System to Visualize Biological Effects for Human Body

NASA Astrophysics Data System (ADS)

Manabe, Seiya; Tenzou, Hideki; Kasuga, Takaaki; Iwakura, Yukiko; Johnston, Robert

2017-09-01

The purpose of this study was to develop a new educational system with an easy-to-use interface in order to support comprehension of the biological effects of radiation on the human body within a short period of time. A paint spray-gun was used as a gamma rays source mock-up for the system. The application screen shows the figure of a human body for radiation deposition using the γ-Sprayer, a virtual radiation source, as well as equivalent dosage and a panel for setting the irradiation conditions. While the learner stands in front of the PC monitor, the virtual radiation source is used to deposit radiation on the graphic of the human body that is displayed. Tissue damage is calculated using an interpolation method from the data calculated by the PHITS simulation code in advance while the learner is pulling the trigger with respect to the irradiation time, incident position, and distance from the screen. It was confirmed that the damage was well represented by the interpolation method. The augmented ?-Spray system was assessed by questionnaire. Pre-post questionnaire was taken for our 41 students in National Institute of Technology, Kagawa College. It was also confirmed that the system has a capability of teaching the basic radiation protection concept, quantitative feeling of the radiation dose, and the biological effects

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.

Systems Biology to Support Nanomaterial Grouping.

PubMed

Riebeling, Christian; Jungnickel, Harald; Luch, Andreas; Haase, Andrea

2017-01-01

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach.

PubMed

St Laurent, Georges; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J L; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R R; Nicolas, Estelle; McCaffrey, Timothy A; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

2016-04-20

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

An integrative strategy for quantitative analysis of the N-glycoproteome in complex biological samples

PubMed Central

2014-01-01

Background The complexity of protein glycosylation makes it difficult to characterize glycosylation patterns on a proteomic scale. In this study, we developed an integrated strategy for comparatively analyzing N-glycosylation/glycoproteins quantitatively from complex biological samples in a high-throughput manner. This strategy entailed separating and enriching glycopeptides/glycoproteins using lectin affinity chromatography, and then tandem labeling them with 18O/16O to generate a mass shift of 6 Da between the paired glycopeptides, and finally analyzing them with liquid chromatography-mass spectrometry (LC-MS) and the automatic quantitative method we developed based on Mascot Distiller. Results The accuracy and repeatability of this strategy were first verified using standard glycoproteins; linearity was maintained within a range of 1:10–10:1. The peptide concentration ratios obtained by the self-build quantitative method were similar to both the manually calculated and theoretical values, with a standard deviation (SD) of 0.023–0.186 for glycopeptides. The feasibility of the strategy was further confirmed with serum from hepatocellular carcinoma (HCC) patients and healthy individuals; the expression of 44 glycopeptides and 30 glycoproteins were significantly different between HCC patient and control serum. Conclusions This strategy is accurate, repeatable, and efficient, and may be a useful tool for identification of disease-related N-glycosylation/glycoprotein changes. PMID:24428921

Quantitative systems toxicology

PubMed Central

Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.

2017-01-01

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440

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.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

PubMed Central

Kassab, Ghassan S.; An, Gary; Sander, Edward A.; Miga, Michael; Guccione, Julius M.; Ji, Songbai; Vodovotz, Yoram

2016-01-01

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for 1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with 2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery. PMID:27015816

Optoelectronic system and apparatus for connection to biological systems

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

Okandan, Murat; Nielson, Gregory N.

The present invention relates to a biological probe structure, as well as apparatuses, systems, and methods employing this structure. In particular embodiments, the structure includes a hermetically sealed unit configured to receive and transmit one or more optical signals. Furthermore, the structure can be implanted subcutaneously and interrogated externally. In this manner, a minimally invasive method can be employed to detect, treat, and/or assess the biological target. Additional methods and systems are also provided.

Advances in systems biology: computational algorithms and applications.

PubMed

Huang, Yufei; Zhao, Zhongming; Xu, Hua; Shyr, Yu; Zhang, Bing

2012-01-01

The 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference featured six technical sessions, one tutorial session, one workshop, and 3 keynote presentations that covered state-of-the-art research activities in genomics, systems biology, and intelligent computing. In addition to a major emphasis on the next generation sequencing (NGS)-driven informatics, ICIBM 2012 aligned significant interests in systems biology and its applications in medicine. We highlight in this editorial the selected papers from the meeting that address the developments of novel algorithms and applications in systems biology.

Water Vapor Uptake of Ultrathin Films of Biologically Derived Nanocrystals: Quantitative Assessment with Quartz Crystal Microbalance and Spectroscopic Ellipsometry.

PubMed

Niinivaara, Elina; Faustini, Marco; Tammelin, Tekla; Kontturi, Eero

2015-11-10

Despite the relevance of water interactions, explicit analysis of vapor adsorption on biologically derived surfaces is often difficult. Here, a system was introduced to study the vapor uptake on a native polysaccharide surface; namely, cellulose nanocrystal (CNC) ultrathin films were examined with a quartz crystal microbalance with dissipation monitoring (QCM-D) and spectroscopic ellipsometry (SE). A significant mass uptake of water vapor by the CNC films was detected using the QCM-D upon increasing relative humidity. In addition, thickness changes proportional to changes in relative humidity were detected using SE. Quantitative analysis of the results attained indicated that in preference to being soaked by water at the point of hydration each individual CNC in the film became enveloped by a 1 nm thick layer of adsorbed water vapor, resulting in the detected thickness response.

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.

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.

Ordinary differential equations with applications in molecular biology.

PubMed

Ilea, M; Turnea, M; Rotariu, M

2012-01-01

Differential equations are of basic importance in molecular biology mathematics because many biological laws and relations appear mathematically in the form of a differential equation. In this article we presented some applications of mathematical models represented by ordinary differential equations in molecular biology. The vast majority of quantitative models in cell and molecular biology are formulated in terms of ordinary differential equations for the time evolution of concentrations of molecular species. Assuming that the diffusion in the cell is high enough to make the spatial distribution of molecules homogenous, these equations describe systems with many participating molecules of each kind. We propose an original mathematical model with small parameter for biological phospholipid pathway. All the equations system includes small parameter epsilon. The smallness of epsilon is relative to the size of the solution domain. If we reduce the size of the solution region the same small epsilon will result in a different condition number. It is clear that the solution for a smaller region is less difficult. We introduce the mathematical technique known as boundary function method for singular perturbation system. In this system, the small parameter is an asymptotic variable, different from the independent variable. In general, the solutions of such equations exhibit multiscale phenomena. Singularly perturbed problems form a special class of problems containing a small parameter which may tend to zero. Many molecular biology processes can be quantitatively characterized by ordinary differential equations. Mathematical cell biology is a very active and fast growing interdisciplinary area in which mathematical concepts, techniques, and models are applied to a variety of problems in developmental medicine and bioengineering. Among the different modeling approaches, ordinary differential equations (ODE) are particularly important and have led to significant advances

A quantitative image cytometry technique for time series or population analyses of signaling networks.

PubMed

Ozaki, Yu-ichi; Uda, Shinsuke; Saito, Takeshi H; Chung, Jaehoon; Kubota, Hiroyuki; Kuroda, Shinya

2010-04-01

Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling. We developed a fully automatable assay technique, termed quantitative image cytometry (QIC), which integrates a quantitative immunostaining technique and a high precision image-processing algorithm for cell identification. With the aid of an automated sample preparation system, this device can quantify protein expression, phosphorylation and localization with subcellular resolution at one-minute intervals. The signaling activities quantified by the assay system showed good correlation with, as well as comparable reproducibility to, western blot analysis. Taking advantage of the high spatio-temporal resolution, we investigated the signaling dynamics of the ERK pathway in PC12 cells. The QIC technique appears as a highly quantitative and versatile technique, which can be a convenient replacement for the most conventional techniques including western blot, flow cytometry and live cell imaging. Thus, the QIC technique can be a powerful tool for investigating the systems biology of cellular signaling.

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

Biological Potential in Serpentinizing Systems

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.

2016-01-01

Generation of the microbial substrate hydrogen during serpentinization, the aqueous alteration of ultramafic rocks, has focused interest on the potential of serpentinizing systems to support biological communities or even the origin of life. However the process also generates considerable alkalinity, a challenge to life, and both pH and hydrogen concentrations vary widely across natural systems as a result of different host rock and fluid composition and differing physical and hydrogeologic conditions. Biological potential is expected to vary in concert. We examined the impact of such variability on the bioenergetics of an example metabolism, methanogenesis, using a cell-scale reactive transport model to compare rates of metabolic energy generation as a function of physicochemical environment. Potential rates vary over more than 5 orders of magnitude, including bioenergetically non-viable conditions, across the range of naturally occurring conditions. In parallel, we assayed rates of hydrogen metabolism in wells associated with the actively serpentinizing Coast Range Ophiolite, which includes conditions more alkaline and considerably less reducing than is typical of serpentinizing systems. Hydrogen metabolism is observed at pH approaching 12 but, consistent with the model predictions, biological methanogenesis is not observed.

Networks’ Characteristics Matter for Systems Biology

PubMed Central

Rider, Andrew K.; Milenković, Tijana; Siwo, Geoffrey H.; Pinapati, Richard S.; Emrich, Scott J.; Ferdig, Michael T.; Chawla, Nitesh V.

2015-01-01

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology. PMID:26500772

Towards Engineering Biological Systems in a Broader Context.

PubMed

Venturelli, Ophelia S; Egbert, Robert G; Arkin, Adam P

2016-02-27

Significant advances have been made in synthetic biology to program information processing capabilities in cells. While these designs can function predictably in controlled laboratory environments, the reliability of these devices in complex, temporally changing environments has not yet been characterized. As human society faces global challenges in agriculture, human health and energy, synthetic biology should develop predictive design principles for biological systems operating in complex environments. Natural biological systems have evolved mechanisms to overcome innumerable and diverse environmental challenges. Evolutionary design rules should be extracted and adapted to engineer stable and predictable ecological function. We highlight examples of natural biological responses spanning the cellular, population and microbial community levels that show promise in synthetic biology contexts. We argue that synthetic circuits embedded in host organisms or designed ecologies informed by suitable measurement of biotic and abiotic environmental parameters could be used as engineering substrates to achieve target functions in complex environments. Successful implementation of these methods will broaden the context in which synthetic biological systems can be applied to solve important problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

DNA DAMAGE QUANTITATION BY ALKALINE GEL ELECTROPHORESIS.

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

SUTHERLAND,B.M.; BENNETT,P.V.; SUTHERLAND, J.C.

2004-03-24

Physical and chemical agents in the environment, those used in clinical applications, or encountered during recreational exposures to sunlight, induce damages in DNA. Understanding the biological impact of these agents requires quantitation of the levels of such damages in laboratory test systems as well as in field or clinical samples. Alkaline gel electrophoresis provides a sensitive (down to {approx} a few lesions/5Mb), rapid method of direct quantitation of a wide variety of DNA damages in nanogram quantities of non-radioactive DNAs from laboratory, field, or clinical specimens, including higher plants and animals. This method stems from velocity sedimentation studies of DNAmore » populations, and from the simple methods of agarose gel electrophoresis. Our laboratories have developed quantitative agarose gel methods, analytical descriptions of DNA migration during electrophoresis on agarose gels (1-6), and electronic imaging for accurate determinations of DNA mass (7-9). Although all these components improve sensitivity and throughput of large numbers of samples (7,8,10), a simple version using only standard molecular biology equipment allows routine analysis of DNA damages at moderate frequencies. We present here a description of the methods, as well as a brief description of the underlying principles, required for a simplified approach to quantitation of DNA damages by alkaline gel electrophoresis.« less

A Philosophical Perspective on Evolutionary Systems Biology

PubMed Central

Soyer, Orkun S.; Siegal, Mark L.

2015-01-01

Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. PMID:26085823

General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters.

PubMed

Hadfield, J D; Nakagawa, S

2010-03-01

Although many of the statistical techniques used in comparative biology were originally developed in quantitative genetics, subsequent development of comparative techniques has progressed in relative isolation. Consequently, many of the new and planned developments in comparative analysis already have well-tested solutions in quantitative genetics. In this paper, we take three recent publications that develop phylogenetic meta-analysis, either implicitly or explicitly, and show how they can be considered as quantitative genetic models. We highlight some of the difficulties with the proposed solutions, and demonstrate that standard quantitative genetic theory and software offer solutions. We also show how results from Bayesian quantitative genetics can be used to create efficient Markov chain Monte Carlo algorithms for phylogenetic mixed models, thereby extending their generality to non-Gaussian data. Of particular utility is the development of multinomial models for analysing the evolution of discrete traits, and the development of multi-trait models in which traits can follow different distributions. Meta-analyses often include a nonrandom collection of species for which the full phylogenetic tree has only been partly resolved. Using missing data theory, we show how the presented models can be used to correct for nonrandom sampling and show how taxonomies and phylogenies can be combined to give a flexible framework with which to model dependence.

Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.

PubMed

Bonham, Kevin S; Stefan, Melanie I

2017-10-01

While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.

Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology

PubMed Central

2017-01-01

While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance. PMID:29023441

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.

Quantitative biological surface science: challenges and recent advances.

PubMed

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

2008-12-23

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

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.

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.

Simultaneous off-axis multiplexed holography and regular fluorescence microscopy of biological cells.

PubMed

Nygate, Yoav N; Singh, Gyanendra; Barnea, Itay; Shaked, Natan T

2018-06-01

We present a new technique for obtaining simultaneous multimodal quantitative phase and fluorescence microscopy of biological cells, providing both quantitative phase imaging and molecular specificity using a single camera. Our system is based on an interferometric multiplexing module, externally positioned at the exit of an optical microscope. In contrast to previous approaches, the presented technique allows conventional fluorescence imaging, rather than interferometric off-axis fluorescence imaging. We demonstrate the presented technique for imaging fluorescent beads and live biological cells.

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

The developmental genetics of biological robustness

PubMed Central

Mestek Boukhibar, Lamia; Barkoulas, Michalis

2016-01-01

Background Living organisms are continuously confronted with perturbations, such as environmental changes that include fluctuations in temperature and nutrient availability, or genetic changes such as mutations. While some developmental systems are affected by such challenges and display variation in phenotypic traits, others continue consistently to produce invariable phenotypes despite perturbation. This ability of a living system to maintain an invariable phenotype in the face of perturbations is termed developmental robustness. Biological robustness is a phenomenon observed across phyla, and studying its mechanisms is central to deciphering the genotype–phenotype relationship. Recent work in yeast, animals and plants has shown that robustness is genetically controlled and has started to reveal the underlying mechinisms behind it. Scope and Conclusions Studying biological robustness involves focusing on an important property of developmental traits, which is the phenotypic distribution within a population. This is often neglected because the vast majority of developmental biology studies instead focus on population aggregates, such as trait averages. By drawing on findings in animals and yeast, this Viewpoint considers how studies on plant developmental robustness may benefit from strict definitions of what is the developmental system of choice and what is the relevant perturbation, and also from clear distinctions between gene effects on the trait mean and the trait variance. Recent advances in quantitative developmental biology and high-throughput phenotyping now allow the design of targeted genetic screens to identify genes that amplify or restrict developmental trait variance and to study how variation propagates across different phenotypic levels in biological systems. The molecular characterization of more quantitative trait loci affecting trait variance will provide further insights into the evolution of genes modulating developmental robustness. The

Electric Field Quantitative Measurement System and Method

NASA Technical Reports Server (NTRS)

Generazio, Edward R. (Inventor)

2016-01-01

A method and system are provided for making a quantitative measurement of an electric field. A plurality of antennas separated from one another by known distances are arrayed in a region that extends in at least one dimension. A voltage difference between at least one selected pair of antennas is measured. Each voltage difference is divided by the known distance associated with the selected pair of antennas corresponding thereto to generate a resulting quantity. The plurality of resulting quantities defined over the region quantitatively describe an electric field therein.

The quantitative and condition-dependent Escherichia coli proteome

PubMed Central

Schmidt, Alexander; Kochanowski, Karl; Vedelaar, Silke; Ahrné, Erik; Volkmer, Benjamin; Callipo, Luciano; Knoops, Kèvin; Bauer, Manuel; Aebersold, Ruedi; Heinemann, Matthias

2016-01-01

Measuring precise concentrations of proteins can provide insights into biological processes. Here, we use efficient protein extraction and sample fractionation and state-of-the-art quantitative mass spectrometry techniques to generate a comprehensive, condition-dependent protein abundance map of Escherichia coli. We measure cellular protein concentrations for 55% of predicted E. coli genes (>2300 proteins) under 22 different experimental conditions and identify methylation and N-terminal protein acetylations previously not known to be prevalent in bacteria. We uncover system-wide proteome allocation, expression regulation, and post-translational adaptations. These data provide a valuable resource for the systems biology and broader E. coli research communities. PMID:26641532

A cell-based systems biology assessment of human blood to monitor immune responses after influenza vaccination.

PubMed

Hoek, Kristen L; Samir, Parimal; Howard, Leigh M; Niu, Xinnan; Prasad, Nripesh; Galassie, Allison; Liu, Qi; Allos, Tara M; Floyd, Kyle A; Guo, Yan; Shyr, Yu; Levy, Shawn E; Joyce, Sebastian; Edwards, Kathryn M; Link, Andrew J

2015-01-01

Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

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.

The common ground of genomics and systems biology

PubMed Central

2014-01-01

The rise of systems biology is intertwined with that of genomics, yet their primordial relationship to one another is ill-defined. We discuss how the growth of genomics provided a critical boost to the popularity of systems biology. We describe the parts of genomics that share common areas of interest with systems biology today in the areas of gene expression, network inference, chromatin state analysis, pathway analysis, personalized medicine, and upcoming areas of synergy as genomics continues to expand its scope across all biomedical fields. PMID:25033072

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.

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

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.

Synthetic Biology: Engineering Living Systems from Biophysical Principles.

PubMed

Bartley, Bryan A; Kim, Kyung; Medley, J Kyle; Sauro, Herbert M

2017-03-28

Synthetic biology was founded as a biophysical discipline that sought explanations for the origins of life from chemical and physical first principles. Modern synthetic biology has been reinvented as an engineering discipline to design new organisms as well as to better understand fundamental biological mechanisms. However, success is still largely limited to the laboratory and transformative applications of synthetic biology are still in their infancy. Here, we review six principles of living systems and how they compare and contrast with engineered systems. We cite specific examples from the synthetic biology literature that illustrate these principles and speculate on their implications for further study. To fully realize the promise of synthetic biology, we must be aware of life's unique properties. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Metabolic modelling in the development of cell factories by synthetic biology

PubMed Central

Jouhten, Paula

2012-01-01

Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory. PMID:24688669

System biology of gene regulation.

PubMed

Baitaluk, Michael

2009-01-01

) questions of biological relevance. Thus systems biology could be treated as such a socioscientific phenomenon and a new approach to both experiments and theory that is defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

Systems biology of cellular membranes: a convergence with biophysics.

PubMed

Chabanon, Morgan; Stachowiak, Jeanne C; Rangamani, Padmini

2017-09-01

Systems biology and systems medicine have played an important role in the last two decades in shaping our understanding of biological processes. While systems biology is synonymous with network maps and '-omics' approaches, it is not often associated with mechanical processes. Here, we make the case for considering the mechanical and geometrical aspects of biological membranes as a key step in pushing the frontiers of systems biology of cellular membranes forward. We begin by introducing the basic components of cellular membranes, and highlight their dynamical aspects. We then survey the functions of the plasma membrane and the endomembrane system in signaling, and discuss the role and origin of membrane curvature in these diverse cellular processes. We further give an overview of the experimental and modeling approaches to study membrane phenomena. We close with a perspective on the converging futures of systems biology and membrane biophysics, invoking the need to include physical variables such as location and geometry in the study of cellular membranes. WIREs Syst Biol Med 2017, 9:e1386. doi: 10.1002/wsbm.1386 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

Quantitative analysis to guide orphan drug development.

PubMed

Lesko, L J

2012-08-01

The development of orphan drugs for rare diseases has made impressive strides in the past 10 years. There has been a surge in orphan drug designations, but new drug approvals have not kept up. This article presents a three-pronged hierarchical strategy for quantitative analysis of data at the descriptive, mechanistic, and systems levels of the biological system that could represent a standardized and rational approach to orphan drug development. Examples are provided to illustrate the concept.

The aims of systems biology: between molecules and organisms.

PubMed

Noble, D

2011-05-01

The systems approach to biology has a long history. Its recent rapid resurgence at the turn of the century reflects the problems encountered in interpreting the sequencing of the genome and the failure of that immense achievement to provide rapid and direct solutions to major multi-factorial diseases. This paper argues that systems biology is necessarily multilevel and that there is no privileged level of causality in biological systems. It is an approach rather than a separate discipline. Functionality arises from biological networks that interact with the genome, the environment and the phenotype. This view of biology is very different from the gene-centred views of neo-Darwinism and molecular biology. In neuroscience, the systems approach leads naturally to 2 important conclusions: first, that the idea of 'programs' in the brain is confusing, and second, that the self is better interpreted as a process than as an object. © Georg Thieme Verlag KG Stuttgart · New York.

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

PubMed Central

Chen, Bor-Sen; Wu, Wei-Sheng

2007-01-01

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

Quantitative nanoscale imaging of orientational order in biological filaments by polarized superresolution microscopy

PubMed Central

Valades Cruz, Cesar Augusto; Shaban, Haitham Ahmed; Kress, Alla; Bertaux, Nicolas; Monneret, Serge; Mavrakis, Manos; Savatier, Julien; Brasselet, Sophie

2016-01-01

Essential cellular functions as diverse as genome maintenance and tissue morphogenesis rely on the dynamic organization of filamentous assemblies. For example, the precise structural organization of DNA filaments has profound consequences on all DNA-mediated processes including gene expression, whereas control over the precise spatial arrangement of cytoskeletal protein filaments is key for mechanical force generation driving animal tissue morphogenesis. Polarized fluorescence is currently used to extract structural organization of fluorescently labeled biological filaments by determining the orientation of fluorescent labels, however with a strong drawback: polarized fluorescence imaging is indeed spatially limited by optical diffraction, and is thus unable to discriminate between the intrinsic orientational mobility of the fluorophore labels and the real structural disorder of the labeled biomolecules. Here, we demonstrate that quantitative single-molecule polarized detection in biological filament assemblies allows not only to correct for the rotational flexibility of the label but also to image orientational order of filaments at the nanoscale using superresolution capabilities. The method is based on polarized direct stochastic optical reconstruction microscopy, using dedicated optical scheme and image analysis to determine both molecular localization and orientation with high precision. We apply this method to double-stranded DNA in vitro and microtubules and actin stress fibers in whole cells. PMID:26831082

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

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.

Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015.

PubMed

Zhao, Zhongming; Liu, Yunlong; Huang, Yufei; Huang, Kun; Ruan, Jianhua

2016-08-26

The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13-15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology.

Systems biology of the structural proteome.

PubMed

Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan; Zhang, Zhen; O'Brien, Edward J; Bliven, Spencer E; Chen, Ke; Chang, Roger L; Bourne, Philip E; Palsson, Bernhard O

2016-03-11

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM

Systems Biology: Impressions from a Newcomer Graduate Student in 2016

ERIC Educational Resources Information Center

Simpson, Melanie Rae

2016-01-01

As a newcomer, the philosophical basis of systems biology seems intuitive and appealing, the underlying philosophy being that the whole of a living system cannot be completely understood by the study of its individual parts. Yet answers to the questions "What is systems biology?" and "What constitutes a systems biology approach in…

Mathematical methods in systems biology.

PubMed

Kashdan, Eugene; Duncan, Dominique; Parnell, Andrew; Schattler, Heinz

2016-12-01

The editors of this Special Issue of Mathematical Biosciences and Engineering were the organizers for the Third International Workshop "Mathematical Methods in System Biology" that took place on June 15-18, 2015 at the University College Dublin in Ireland. As stated in the workshop goals, we managed to attract a good mix of mathematicians and statisticians working on biological and medical applications with biologists and clinicians interested in presenting their challenging problems and looking to find mathematical and statistical tools for their solutions.

Single cell systems biology by super-resolution imaging and combinatorial labeling

PubMed Central

Lubeck, Eric; Cai, Long

2012-01-01

Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral separability of fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using Fluorescence in situ Hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured the mRNA levels of 32 genes simultaneously in single S. cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells provides a natural approach to bring systems biology into single cells. PMID:22660740

The Spring of Systems Biology-Driven Breeding.

PubMed

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

2018-05-12

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

Applying differential dynamic logic to reconfigurable biological networks.

PubMed

Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

2017-09-01

Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

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

A Quantitative Approach to the Formal Verification of Real-Time Systems.

DTIC Science & Technology

1996-09-01

Computer Science A Quantitative Approach to the Formal Verification of Real - Time Systems Sergio Vale Aguiar Campos September 1996 CMU-CS-96-199...ptisiic raieaiSI v Diambimos Lboiamtad _^ A Quantitative Approach to the Formal Verification of Real - Time Systems Sergio Vale Aguiar Campos...implied, of NSF, the Semiconduc- tor Research Corporation, ARPA or the U.S. government. Keywords: real - time systems , formal verification, symbolic

Monitoring of environmental UV radiation by biological dosimeters

NASA Astrophysics Data System (ADS)

Rontó, Gy.; Bérces, A.; Gróf, P.; Fekete, A.; Kerékgyártó, T.; Gáspár, S.; Stick, C.

As a consequence of the stratospheric ozone layer depletion biological systems can be damaged due to increased UV-B radiation. The aim of biological dosimetry is to establish a quantitative basis for the risk assessment of the biosphere. DNA is the most important target molecule of biological systems having special sensitivity against short wavelength components of the environmental radiation. Biological dosimeters are usually simple organisms, or components of them, modeling the cellular DNA. Phage T7 and polycrystalline uracil biological dosimeters have been developed and used in our laboratory for monitoring the environmental radiation in different radiation conditions (from the polar to equatorial regions). Comparisons with Robertson-Berger (RB) meter data, as well as with model calculation data weighted by the corresponding spectral sensitivities of the dosimeters are presented. Suggestion is given how to determine the trend of the increase in the biological risk due to ozone depletion.

Universality and predictability in molecular quantitative genetics.

PubMed

Nourmohammad, Armita; Held, Torsten; Lässig, Michael

2013-12-01

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology. Copyright © 2013. Published by Elsevier Ltd.

Engineering plant metabolism into microbes: from systems biology to synthetic biology.

PubMed

Xu, Peng; Bhan, Namita; Koffas, Mattheos A G

2013-04-01

Plant metabolism represents an enormous repository of compounds that are of pharmaceutical and biotechnological importance. Engineering plant metabolism into microbes will provide sustainable solutions to produce pharmaceutical and fuel molecules that could one day replace substantial portions of the current fossil-fuel based economy. Metabolic engineering entails targeted manipulation of biosynthetic pathways to maximize yields of desired products. Recent advances in Systems Biology and the emergence of Synthetic Biology have accelerated our ability to design, construct and optimize cell factories for metabolic engineering applications. Progress in predicting and modeling genome-scale metabolic networks, versatile gene assembly platforms and delicate synthetic pathway optimization strategies has provided us exciting opportunities to exploit the full potential of cell metabolism. In this review, we will discuss how systems and synthetic biology tools can be integrated to create tailor-made cell factories for efficient production of natural products and fuel molecules in microorganisms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects.

PubMed

Liu, Yanfeng; Shin, Hyun-dong; Li, Jianghua; Liu, Long

2015-02-01

Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advances in Structural Biology and the Application to Biological Filament Systems.

PubMed

Popp, David; Koh, Fujiet; Scipion, Clement P M; Ghoshdastider, Umesh; Narita, Akihiro; Holmes, Kenneth C; Robinson, Robert C

2018-04-01

Structural biology has experienced several transformative technological advances in recent years. These include: development of extremely bright X-ray sources (microfocus synchrotron beamlines and free electron lasers) and the use of electrons to extend protein crystallography to ever decreasing crystal sizes; and an increase in the resolution attainable by cryo-electron microscopy. Here we discuss the use of these techniques in general terms and highlight their application for biological filament systems, an area that is severely underrepresented in atomic resolution structures. We assemble a model of a capped tropomyosin-actin minifilament to demonstrate the utility of combining structures determined by different techniques. Finally, we survey the methods that attempt to transform high resolution structural biology into more physiological environments, such as the cell. Together these techniques promise a compelling decade for structural biology and, more importantly, they will provide exciting discoveries in understanding the designs and purposes of biological machines. © 2018 The Authors. BioEssays Published by WILEY Periodicals, Inc.

Systems biology for molecular life sciences and its impact in biomedicine.

PubMed

Medina, Miguel Ángel

2013-03-01

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

A SYSTEMS BIOLOGY APPROACH TO DEVELOPMENTAL TOXICOLOGY

EPA Science Inventory

Abstract
Recent advances in developmental biology have yielded detailed models of gene regulatory networks (GRNs) involved in cell specification and other processes in embryonic differentiation. Such networks form the bedrock on which a systems biology approach to developme...

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.

Plant Systems Biology (editorial)

USDA-ARS?s Scientific Manuscript database

In June 2003, Plant Physiology published an Arabidopsis special issue devoted to plant systems biology. The intention of Natasha Raikhel and Gloria Coruzzi, the two editors of this first-of-its-kind issue, was ‘‘to help nucleate this new effort within the plant community’’ as they considered that ‘‘...

Multilayer network modeling of integrated biological systems. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

De Domenico, Manlio

2018-03-01

Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks - obtained by aggregating or neglecting temporal or categorical description of biological data - are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.

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.

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.

Dietary antioxidant synergy in chemical and biological systems.

PubMed

Wang, Sunan; Zhu, Fan

2017-07-24

Antioxidant (AOX) synergies have been much reported in chemical ("test-tube" based assays focusing on pure chemicals), biological (tissue culture, animal and clinical models), and food systems during the past decade. Tentative synergies differ from each other due to the composition of AOX and the quantification methods. Regeneration mechanism responsible for synergy in chemical systems has been discussed. Solvent effects could contribute to the artifacts of synergy observed in the chemical models. Synergy in chemical models may hardly be relevant to biological systems that have been much less studied. Apparent discrepancies exist in understanding the molecular mechanisms in both chemical and biological systems. This review discusses diverse variables associated with AOX synergy and molecular scenarios for explanation. Future research to better utilize the synergy is suggested.

Controlled biological and biomimetic systems for landmine detection.

PubMed

Habib, Maki K

2007-08-30

Humanitarian demining requires to accurately detect, locate and deactivate every single landmine and other buried mine-like objects as safely and as quickly as possible, and in the most non-invasive manner. The quality of landmine detection affects directly the efficiency and safety of this process. Most of the available methods to detect explosives and landmines are limited by their sensitivity and/or operational complexities. All landmines leak with time small amounts of their explosives that can be found on surrounding ground and plant life. Hence, explosive signatures represent the robust primary indicator of landmines. Accordingly, developing innovative technologies and efficient techniques to identify in real-time explosives residue in mined areas represents an attractive and promising approach. Biological and biologically inspired detection technology has the potential to compete with or be used in conjunction with other artificial technology to complement performance strengths. Biological systems are sensitive to many different scents concurrently, a property that has proven difficult to replicate artificially. Understanding biological systems presents unique opportunities for developing new capabilities through direct use of trained bio-systems, integration of living and non-living components, or inspiring new design by mimicking biological capabilities. It is expected that controlled bio-systems, biotechnology and microbial techniques will contribute to the advancement of mine detection and other application domains. This paper provides directions, evaluation and analysis on the progress of controlled biological and biomimetic systems for landmine detection. It introduces and discusses different approaches developed, underlining their relative advantages and limitations, and highlighting trends, safety and ecology concern, and possible future directions.

Tracing organizing principles: learning from the history of systems biology.

PubMed

Green, Sara; Wolkenhauer, Olaf

2013-01-01

With the emergence of systems biology, the identification of organizing principles is being highlighted as a key research aim. Researchers attempt to "reverse engineer" the functional organization of biological systems using methodologies from mathematics, engineering and computer science while taking advantage of data produced by new experimental techniques. While systems biology is a relatively new approach, the quest for general principles of biological organization dates back to systems theoretic approaches in early and mid-twentieth century. The aim of this paper is to draw on this historical background in order to increase the understanding of the motivation behind the search for general principles and to clarify different epistemic aims within systems biology. We pinpoint key aspects of earlier approaches that also underlie the current practice. These are i) the focus on relational and system-level properties, ii) the inherent critique of reductionism and fragmentation of knowledge resulting from overspecialization, and iii) the insight that the ideal of formulating abstract organizing principles is complementary to, rather than conflicting with, the aim of formulating detailed explanations of biological mechanisms. We argue that looking back not only helps us understand the current practice but also points to possible future directions for systems biology.

Set membership experimental design for biological systems.

PubMed

Marvel, Skylar W; Williams, Cranos M

2012-03-21

Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify

Set membership experimental design for biological systems

PubMed Central

2012-01-01

Background Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. Results In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. Conclusions The practicability of our approach is illustrated with a case study. This study shows that our

Quantitative analysis of three-dimensional biological cells using interferometric microscopy

NASA Astrophysics Data System (ADS)

Shaked, Natan T.; Wax, Adam

2011-06-01

Live biological cells are three-dimensional microscopic objects that constantly adjust their sizes, shapes and other biophysical features. Wide-field digital interferometry (WFDI) is a holographic technique that is able to record the complex wavefront of the light which has interacted with in-vitro cells in a single camera exposure, where no exogenous contrast agents are required. However, simple quasi-three-dimensional holographic visualization of the cell phase profiles need not be the end of the process. Quantitative analysis should permit extraction of numerical parameters which are useful for cytology or medical diagnosis. Using a transmission-mode setup, the phase profile represents the multiplication between the integral refractive index and the thickness of the sample. These coupled variables may not be distinct when acquiring the phase profiles of dynamic cells. Many morphological parameters which are useful for cell biologists are based on the cell thickness profile rather than on its phase profile. We first overview methods to decouple the cell thickness and its refractive index using the WFDI-based phase profile. Then, we present a whole-cell-imaging approach which is able to extract useful numerical parameters on the cells even in cases where decoupling of cell thickness and refractive index is not possible or desired.

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

SEEK: a systems biology data and model management platform.

PubMed

Wolstencroft, Katherine; Owen, Stuart; Krebs, Olga; Nguyen, Quyen; Stanford, Natalie J; Golebiewski, Martin; Weidemann, Andreas; Bittkowski, Meik; An, Lihua; Shockley, David; Snoep, Jacky L; Mueller, Wolfgang; Goble, Carole

2015-07-11

Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and

Intracellular subsurface imaging using a hybrid shear-force feedback/scanning quantitative phase microscopy technique

NASA Astrophysics Data System (ADS)

Edward, Kert

Quantitative phase microscopy (QPM) allows for the imaging of translucent or transparent biological specimens without the need for exogenous contrast agents. This technique is usually applied towards the investigation of simple cells such as red blood cells which are typically enucleated and can be considered to be homogenous. However, most biological cells are nucleated and contain other interesting intracellular organelles. It has been established that the physical characteristics of certain subsurface structures such as the shape and roughness of the nucleus is well correlated with onset and progress of pathological conditions such as cancer. Although the acquired quantitative phase information of biological cells contains surface information as well as coupled subsurface information, the latter has been ignored up until now. A novel scanning quantitative phase imaging system unencumbered by 2pi ambiguities is hereby presented. This system is incorporated into a shear-force feedback scheme which allows for simultaneous phase and topography determination. It will be shown how subsequent image processing of these two data sets allows for the extraction of the subsurface component in the phase data and in vivo cell refractometry studies. Both fabricated samples and biological cells ranging from rat fibroblast cells to malaria infected human erythrocytes were investigated as part of this research. The results correlate quite well with that obtained via other microscopy techniques.

Quantitative genetics in natural populations: Means of monitoring natural biological processes

Treesearch

Brook G. Milligan

2001-01-01

One of the goals of conservation biology is to maintain the integrity of natural processes in populations of rare plants. In the short term one of the main concerns is often whether the mating system of rare plants is disrupted, for example, by fragmentation. In the long term one of the main concerns is often whether small isolated populations maintain enough genetic...

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

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.

Multiscale systems biology of trauma-induced coagulopathy.

PubMed

Tsiklidis, Evan; Sims, Carrie; Sinno, Talid; Diamond, Scott L

2018-07-01

Trauma with hypovolemic shock is an extreme pathological state that challenges the body to maintain blood pressure and oxygenation in the face of hemorrhagic blood loss. In conjunction with surgical actions and transfusion therapy, survival requires the patient's blood to maintain hemostasis to stop bleeding. The physics of the problem are multiscale: (a) the systemic circulation sets the global blood pressure in response to blood loss and resuscitation therapy, (b) local tissue perfusion is altered by localized vasoregulatory mechanisms and bleeding, and (c) altered blood and vessel biology resulting from the trauma as well as local hemodynamics control the assembly of clotting components at the site of injury. Building upon ongoing modeling efforts to simulate arterial or venous thrombosis in a diseased vasculature, computer simulation of trauma-induced coagulopathy is an emerging approach to understand patient risk and predict response. Despite uncertainties in quantifying the patient's dynamic injury burden, multiscale systems biology may help link blood biochemistry at the molecular level to multiorgan responses in the bleeding patient. As an important goal of systems modeling, establishing early metrics of a patient's high-dimensional trajectory may help guide transfusion therapy or warn of subsequent later stage bleeding or thrombotic risks. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Regulatory Biology Models of Systems Properties and Processes > Mechanistic Models. © 2018 Wiley Periodicals, Inc.

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.

Nonlinear characterization of elasticity using quantitative optical coherence elastography.

PubMed

Qiu, Yi; Zaki, Farzana R; Chandra, Namas; Chester, Shawn A; Liu, Xuan

2016-11-01

Optical coherence elastography (OCE) has been used to perform mechanical characterization on biological tissue at the microscopic scale. In this work, we used quantitative optical coherence elastography (qOCE), a novel technology we recently developed, to study the nonlinear elastic behavior of biological tissue. The qOCE system had a fiber-optic probe to exert a compressive force to deform tissue under the tip of the probe. Using the space-division multiplexed optical coherence tomography (OCT) signal detected by a spectral domain OCT engine, we were able to simultaneously quantify the probe deformation that was proportional to the force applied, and to quantify the tissue deformation. In other words, our qOCE system allowed us to establish the relationship between mechanical stimulus and tissue response to characterize the stiffness of biological tissue. Most biological tissues have nonlinear elastic behavior, and the apparent stress-strain relationship characterized by our qOCE system was nonlinear an extended range of strain, for a tissue-mimicking phantom as well as biological tissues. Our experimental results suggested that the quantification of force in OCE was critical for accurate characterization of tissue mechanical properties and the qOCE technique was capable of differentiating biological tissues based on the elasticity of tissue that is generally nonlinear.

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

An online model composition tool for system biology models

PubMed Central

2013-01-01

Background There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models. Results We present the design and implementation of the Model Composition Tool (Interface) within the PathCase-SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user’s input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models. Conclusions Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well. PMID:24006914

Exploring Synthetic and Systems Biology at the University of Edinburgh.

PubMed

Fletcher, Liz; Rosser, Susan; Elfick, Alistair

2016-06-15

The Centre for Synthetic and Systems Biology ('SynthSys') was originally established in 2007 as the Centre for Integrative Systems Biology, funded by the Biotechnology and Biological Sciences Research Council (BBSRC) and the Engineering and Physical Sciences Research Council (EPSRC). Today, SynthSys embraces an extensive multidisciplinary community of more than 200 researchers from across the University with a common interest in synthetic and systems biology. Our research is broad and deep, addressing a diversity of scientific questions, with wide ranging impact. We bring together the power of synthetic biology and systems approaches to focus on three core thematic areas: industrial biotechnology, agriculture and the environment, and medicine and healthcare. In October 2015, we opened a newly refurbished building as a physical hub for our new U.K. Centre for Mammalian Synthetic Biology funded by the BBSRC/EPSRC/MRC as part of the U.K. Research Councils' Synthetic Biology for Growth programme. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

PubMed

Roehner, Nicholas; Zhang, Zhen; Nguyen, Tramy; Myers, Chris J

2015-08-21

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

A Quantitative Systems Pharmacology Approach to Infer Pathways Involved in Complex Disease Phenotypes.

PubMed

Schurdak, Mark E; Pei, Fen; Lezon, Timothy R; Carlisle, Diane; Friedlander, Robert; Taylor, D Lansing; Stern, Andrew M

2018-01-01

Designing effective therapeutic strategies for complex diseases such as cancer and neurodegeneration that involve tissue context-specific interactions among multiple gene products presents a major challenge for precision medicine. Safe and selective pharmacological modulation of individual molecular entities associated with a disease often fails to provide efficacy in the clinic. Thus, development of optimized therapeutic strategies for individual patients with complex diseases requires a more comprehensive, systems-level understanding of disease progression. Quantitative systems pharmacology (QSP) is an approach to drug discovery that integrates computational and experimental methods to understand the molecular pathogenesis of a disease at the systems level more completely. Described here is the chemogenomic component of QSP for the inference of biological pathways involved in the modulation of the disease phenotype. The approach involves testing sets of compounds of diverse mechanisms of action in a disease-relevant phenotypic assay, and using the mechanistic information known for the active compounds, to infer pathways and networks associated with the phenotype. The example used here is for monogenic Huntington's disease (HD), which due to the pleiotropic nature of the mutant phenotype has a complex pathogenesis. The overall approach, however, is applicable to any complex disease.

PREFACE: Nanobiology: from physics and engineering to biology

NASA Astrophysics Data System (ADS)

Nussinov, Ruth; Alemán, Carlos

2006-03-01

Biological systems are inherently nano in scale. Unlike nanotechnology, nanobiology is characterized by the interplay between physics, materials science, synthetic organic chemistry, engineering and biology. Nanobiology is a new discipline, with the potential of revolutionizing medicine: it combines the tools, ideas and materials of nanoscience and biology; it addresses biological problems that can be studied and solved by nanotechnology; it devises ways to construct molecular devices using biomacromolecules; and it attempts to build molecular machines utilizing concepts seen in nature. Its ultimate aim is to be able to predictably manipulate these, tailoring them to specified needs. Nanobiology targets biological systems and uses biomacromolecules. Hence, on the one hand, nanobiology is seemingly constrained in its scope as compared to general nanotechnology. Yet the amazing intricacy of biological systems, their complexity, and the richness of the shapes and properties provided by the biological polymers, enrich nanobiology. Targeting biological systems entails comprehension of how they work and the ability to use their components in design. From the physical standpoint, ultimately, if we are to understand biology we need to learn how to apply physical principles to figure out how these systems actually work. The goal of nanobiology is to assist in probing these systems at the appropriate length scale, heralding a new era in the biological, physical and chemical sciences. Biology is increasingly asking quantitative questions. Quantitation is essential if we are to understand how the cell works, and the details of its regulation. The physical sciences provide tools and strategies to obtain accurate measurements and simulate the information to allow comprehension of the processes. Nanobiology is at the interface of the physical and the biological sciences. Biology offers to the physical sciences fascinating problems, sophisticated systems and a rich repertoire of

Modeling autism: a systems biology approach

PubMed Central

2012-01-01

Autism is the fastest growing developmental disorder in the world today. The prevalence of autism in the US has risen from 1 in 2500 in 1970 to 1 in 88 children today. People with autism present with repetitive movements and with social and communication impairments. These impairments can range from mild to profound. The estimated total lifetime societal cost of caring for one individual with autism is $3.2 million US dollars. With the rapid growth in this disorder and the great expense of caring for those with autism, it is imperative for both individuals and society that techniques be developed to model and understand autism. There is increasing evidence that those individuals diagnosed with autism present with highly diverse set of abnormalities affecting multiple systems of the body. To this date, little to no work has been done using a whole body systems biology approach to model the characteristics of this disorder. Identification and modelling of these systems might lead to new and improved treatment protocols, better diagnosis and treatment of the affected systems, which might lead to improved quality of life by themselves, and, in addition, might also help the core symptoms of autism due to the potential interconnections between the brain and nervous system with all these other systems being modeled. This paper first reviews research which shows that autism impacts many systems in the body, including the metabolic, mitochondrial, immunological, gastrointestinal and the neurological. These systems interact in complex and highly interdependent ways. Many of these disturbances have effects in most of the systems of the body. In particular, clinical evidence exists for increased oxidative stress, inflammation, and immune and mitochondrial dysfunction which can affect almost every cell in the body. Three promising research areas are discussed, hierarchical, subgroup analysis and modeling over time. This paper reviews some of the systems disturbed in autism and

Quantitative volumetric Raman imaging of three dimensional cell cultures

NASA Astrophysics Data System (ADS)

Kallepitis, Charalambos; Bergholt, Mads S.; Mazo, Manuel M.; Leonardo, Vincent; Skaalure, Stacey C.; Maynard, Stephanie A.; Stevens, Molly M.

2017-03-01

The ability to simultaneously image multiple biomolecules in biologically relevant three-dimensional (3D) cell culture environments would contribute greatly to the understanding of complex cellular mechanisms and cell-material interactions. Here, we present a computational framework for label-free quantitative volumetric Raman imaging (qVRI). We apply qVRI to a selection of biological systems: human pluripotent stem cells with their cardiac derivatives, monocytes and monocyte-derived macrophages in conventional cell culture systems and mesenchymal stem cells inside biomimetic hydrogels that supplied a 3D cell culture environment. We demonstrate visualization and quantification of fine details in cell shape, cytoplasm, nucleus, lipid bodies and cytoskeletal structures in 3D with unprecedented biomolecular specificity for vibrational microspectroscopy.

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

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.

Engineering challenges of BioNEMS: the integration of microfluidics, micro- and nanodevices, models and external control for systems biology.

PubMed

Wikswo, J P; Prokop, A; Baudenbacher, F; Cliffel, D; Csukas, B; Velkovsky, M

2006-08-01

Systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 10(6) dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 10(6) model parameters. Except for fluorescence and micro-electrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro- and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth; secondly, the ability to open existing internal control and signalling loops; thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators; and, fourthly, real-time, closed-loop, single-cell control algorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.

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

Advanced systems biology methods in drug discovery and translational biomedicine.

PubMed

Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

2013-01-01

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Cancer systems biology: signal processing for cancer research

PubMed Central

Yli-Harja, Olli; Ylipää, Antti; Nykter, Matti; Zhang, Wei

2011-01-01

In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts. PMID:21439242

Workshop Report: Systems Biology for Organotypic Cell Cultures

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

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Workshop Report: Systems Biology for Organotypic Cell Cultures

DOE PAGES

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph; ...

2016-11-14

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.

PubMed

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

2018-03-26

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

The role of mechanics in biological and bio-inspired systems.

PubMed

Egan, Paul; Sinko, Robert; LeDuc, Philip R; Keten, Sinan

2015-07-06

Natural systems frequently exploit intricate multiscale and multiphasic structures to achieve functionalities beyond those of man-made systems. Although understanding the chemical make-up of these systems is essential, the passive and active mechanics within biological systems are crucial when considering the many natural systems that achieve advanced properties, such as high strength-to-weight ratios and stimuli-responsive adaptability. Discovering how and why biological systems attain these desirable mechanical functionalities often reveals principles that inform new synthetic designs based on biological systems. Such approaches have traditionally found success in medical applications, and are now informing breakthroughs in diverse frontiers of science and engineering.

Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone.

PubMed

Geerts, Hugo; Roberts, Patrick; Spiros, Athan; Potkin, Steven

2015-04-01

The concept of targeted therapies remains a holy grail for the pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide quantitative systems pharmacology as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis and applied this to the case of iloperidone in schizophrenia. This approach implements the actual neurophysiological effect of genotypes in a computer-based biophysically realistic model of human neuronal circuits, is parameterized with human imaging and pathology, and is calibrated by clinical data. We keep the drug pharmacology constant, but allowed the biological model coupling values to fluctuate in a restricted range around their calibrated values, thereby simulating random genetic mutations and representing variability in patient response. Using hypothesis-free Design of Experiments methods the dopamine D4 R-AMPA (receptor-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor coupling in cortical neurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional pharmacogenomics analysis. The serotonin 5-HT3 receptor-mediated effect on interneuron gamma-aminobutyric acid conductance was identified as the process that moderately drove the differentiation of iloperidone versus ziprasidone. This paper suggests that reverse-engineered quantitative systems pharmacology is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets. © The Author(s) 2015.

Holarchical Systems and Emotional Holons : Biologically-Inspired System Designs for Control of Autonomous Aerial Vehicles

NASA Technical Reports Server (NTRS)

Ippolito, Corey; Plice, Laura; Pisanich, Greg

2003-01-01

The BEES (Bio-inspired Engineering for Exploration Systems) for Mars project at NASA Ames Research Center has the goal of developing bio-inspired flight control strategies to enable aerial explorers for Mars scientific investigations. This paper presents a summary of our ongoing research into biologically inspired system designs for control of unmanned autonomous aerial vehicle communities for Mars exploration. First, we present cooperative design considerations for robotic explorers based on the holarchical nature of biological systems and communities. Second, an outline of an architecture for cognitive decision making and control of individual robotic explorers is presented, modeled after the emotional nervous system of cognitive biological systems. Keywords: Holarchy, Biologically Inspired, Emotional UAV Flight Control

Collaborative Systems Biology Projects for the Military Medical Community.

PubMed