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‣ A Transformative Model for Undergraduate Quantitative Biology Education

Usher, David C.; Driscoll, Tobin A.; Dhurjati, Prasad; Pelesko, John A.; Rossi, Louis F.; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B.
Fonte: American Society for Cell Biology Publicador: American Society for Cell Biology
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
Relevância na Pesquisa
57.169033%
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) creating a new interdisciplinary major, quantitative biology, designed for students interested in solving complex biological problems using advanced mathematical approaches. To develop the bio-calculus sections, the Department of Mathematical Sciences revised its three-semester calculus sequence to include differential equations in the first semester and, rather than using examples traditionally drawn from application domains that are most relevant to engineers, drew models and examples heavily from the life sciences. The curriculum of the B.S. degree in Quantitative Biology was designed to provide students with a solid foundation in biology, chemistry, and mathematics, with an emphasis on preparation for research careers in life sciences. Students in the program take core courses from biology, chemistry, and physics, though mathematics, as the cornerstone of all quantitative sciences, is given particular prominence. Seminars and a capstone course stress how the interplay of mathematics and biology can be used to explain complex biological systems. To initiate these academic changes required the identification of barriers and the implementation of solutions.

‣ Quantitative cell biology: the essential role of theory

Howard, Jonathon
Fonte: The American Society for Cell Biology Publicador: The American Society for Cell Biology
Tipo: Artigo de Revista Científica
Publicado em 05/11/2014 Português
Relevância na Pesquisa
47.246943%
Quantitative biology is a hot area, as evidenced by the recent establishment of institutes, graduate programs, and conferences with that name. But what is quantitative biology? What should it be? And how can it contribute to solving the big questions in biology? The past decade has seen very rapid development of quantitative experimental techniques, especially at the single-molecule and single-cell levels. In this essay, I argue that quantitative biology is much more than just the quantitation of these experimental results. Instead, it should be the application of the scientific method by which measurement is directed toward testing theories. In this view, quantitative biology is the recognition that theory and models play critical roles in biology, as they do in physics and engineering. By tying together experiment and theory, quantitative biology promises a deeper understanding of underlying mechanisms, when the theory works, or to new discoveries, when it does not.

‣ Quantitative biology: where modern biology meets physical sciences

Shekhar, Shashank; Zhu, Lian; Mazutis, Linas; Sgro, Allyson E.; Fai, Thomas G.; Podolski, Marija
Fonte: The American Society for Cell Biology Publicador: The American Society for Cell Biology
Tipo: Artigo de Revista Científica
Publicado em 05/11/2014 Português
Relevância na Pesquisa
57.14856%
Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.

‣ Quantitative biology: where modern biology meets physical sciences

Shekhar, Shashank; Zhu, Lian; Mazutis, Linas; Sgro, Allyson E.; Fai, Thomas G.; Podolski, Marija
Fonte: The American Society for Cell Biology Publicador: The American Society for Cell Biology
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
57.14856%
Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.

‣ Teaching quantitative biology: goals, assessments, and resources

Aikens, Melissa L.; Dolan, Erin L.
Fonte: The American Society for Cell Biology Publicador: The American Society for Cell Biology
Tipo: Artigo de Revista Científica
Publicado em 05/11/2014 Português
Relevância na Pesquisa
57.14924%
More than a decade has passed since the publication of BIO2010, calling for an increased emphasis on quantitative skills in the undergraduate biology curriculum. In that time, relatively few papers have been published that describe educational innovations in quantitative biology or provide evidence of their effects on students. Using a “backward design” framework, we lay out quantitative skill and attitude goals, assessment strategies, and teaching resources to help biologists teach more quantitatively. Collaborations between quantitative biologists and education researchers are necessary to develop a broader and more appropriate suite of assessment tools, and to provide much-needed evidence on how particular teaching strategies affect biology students' quantitative skill development and attitudes toward quantitative work.

‣ TinkerCell: Modular CAD Tool for Synthetic Biology

Chandran, Deepak; Bergmann, Frank T.; Sauro, Herbert M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/07/2009 Português
Relevância na Pesquisa
47.002427%
Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. An application named TinkerCell has been created in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various C and Python programs that are hosted by TinkerCell via an extensive C and Python API. TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. Because TinkerCell associates parameters and equations in a model with their respective part, parts can be loaded from databases along with their parameters and rate equations. The modular network design can be used to exchange modules as well as test the concept of modularity in biological systems. The flexible modeling framework along with the C and Python API allows TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads...

‣ A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

Kim, Seyoung; Sohn, Kyung-Ah; Xing, Eric P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/11/2008 Português
Relevância na Pesquisa
47.004146%
Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to address this issue in a principled way. Our approach explicitly represents the dependency structure among the quantitative traits as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical method, and borrow information across correlated phenotypes to discover the genetic markers that perturbe a subset of correlated triats jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis...

‣ Quantitative Measure of Stability in Gene Regulatory Networks

Ao, P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/07/2005 Português
Relevância na Pesquisa
57.0306%
A quantitative measure of stability in stochastic dynamics starts to emerge in recent experiments on bioswitches. This quantity, similar to the potential function in mathematics, is deeply rooted in biology, dated back at the beginning of quantitative description of biological processes: the adaptive landscape of Wright (1932) and the development landscape of Waddington (1940). Nevertheless, its quantitative implication has been frequently challenged by biologists. Recent progresses in quantitative biology begin to meet those outstanding challenges.

‣ Efficiency, Robustness and Stochasticity of Gene Regulatory Networks in Systems Biology: lambda Switch as a Working Example

Zhu, X.; Yin, L.; Hood, L.; Galas, D.; Ao, P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.18355%
Phage lambda is one of the most studied biological models in modern molecular biology. Over the past 50 years quantitative experimental knowledge on this biological model has been accumulated at all levels: physics, chemistry, genomics, proteomics, functions, and more. All its components have been known to a great detail. The theoretical task has been to integrate its components to make the organism working quantitatively in a harmonic manner. This would test our biological understanding and would lay a solid fundamental for further explorations and applications, an obvious goal of systems biology. One of the outstanding challenges in doing so has been the so-called stability puzzle of lambda switch: the biologically observed robustness and its difficult mathematical reconstruction based on known experimental values. In this chapter we review the recent theoretical and experimental efforts on tackling this problem. An emphasis is put on the minimum quantitative modeling where a successful numerical agreement between experiments and modeling has been achieved. A novel method tentatively named stochastic dynamical structure analysis emerged from such study is also discussed within a broad modeling perspective.; Comment: 40 pages

‣ Athena: Modular CAM/CAD Software for Synthetic Biology

Chandran, Deepak; Bergmann, Frank T.; Sauro, Herbert M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/02/2009 Português
Relevância na Pesquisa
47.065527%
Synthetic biology is the engineering of cellular networks. It combines principles of engineering and the knowledge of biological networks to program the behavior of cells. Computational modeling techniques in conjunction with molecular biology techniques have been successful in constructing biological devices such as switches, oscillators, and gates. The ambition of synthetic biology is to construct complex systems from such fundamental devices, much in the same way electronic circuits are built from basic parts. As this ambition becomes a reality, engineering concepts such as interchangeable parts and encapsulation will find their way into biology. We realize that there is a need for computational tools that would support such engineering concepts in biology. As a solution, we have developed the software Athena that allows biological models to be constructed as modules. Modules can be connected to one another without altering the modules themselves. In addition, Athena houses various tools useful for designing synthetic networks including tools to perform simulations, automatically derive transcription rate expressions, and view and edit synthetic DNA sequences. New tools can be incorporated into Athena without modifying existing program via a plugin interface...

‣ Newton-type Methods for REML Estimation in Genetic Analysis of Quantitative Traits

Mishchenko, Kateryna; Holmgren, Sverker; Ronnegard, Lars
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/11/2007 Português
Relevância na Pesquisa
47.004146%
Robust and efficient optimization methods for variance component estimation using Restricted Maximum Likelihood (REML) models for genetic mapping of quantitative traits are considered. We show that the standard Newton-AI scheme may fail when the optimum is located at one of the constraint boundaries, and we introduce different approaches to remedy this by taking the constraints into account. We approximate the Hessian of the objective function using the average information matrix and also by using an inverse BFGS formula. The robustness and efficiency is evaluated for problems derived from two experimental data from the same animal populations.; Comment: 20 pages, 7 figures, 3 tables

‣ The EM Algorithm and the Rise of Computational Biology

Fan, Xiaodan; Yuan, Yuan; Liu, Jun S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/04/2011 Português
Relevância na Pesquisa
47.002427%
In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.; Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

‣ Universally Sloppy Parameter Sensitivities in Systems Biology

Gutenkunst, Ryan N.; Waterfall, Joshua J.; Casey, Fergal P.; Brown, Kevin S.; Myers, Christopher R.; Sethna, James P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.10011%
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 \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other 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...

‣ Integration of Omics Data and Systems Biology Modeling: Effect of Cyclosporine A on the Nrf2 Pathway in Human Renal Kidneys Cells

Hamon, Jérémy; Jennings, Paul; Bois, Frederic Y.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/12/2013 Português
Relevância na Pesquisa
47.087793%
In a recent paper, Wilmes et al. demonstrated a qualitative integration of omics data streams to gain a mechanistic understanding of cyclosporine A toxicity. One of their major conclusions was that cyclosporine A strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells exposed in vitro. We pursue here the analysis of those data with a quantitative integration of omics data with a differential equation model of the Nrf2 pathway. That was done in two steps: (i) Modeling the in vitro pharmacokinetics of cyclosporine A (exchange between cells, culture medium and vial walls) with a minimal distribution model. (ii) Modeling the time course of omics markers in response to cyclosporine A exposure at the cell level with a coupled PK-systems biology model. Posterior statistical distributions of the parameter values were obtained by Markov chain Monte Carlo sampling. Data were well simulated, and the known in vitro toxic effect EC50 was well matched by model predictions. The integration of in vitro pharmacokinetics and systems biology modeling gives us a quantitative insight into mechanisms of cyclosporine A oxidative-stress induction, and a way to predict such a stress for a variety of exposure conditions.; Comment: Six figures...

‣ Computer modeling of feelings and emotions: a quantitative neural network model of the feeling-of-knowing

Gopych, Petro M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/06/2002 Português
Relevância na Pesquisa
47.004146%
The first quantitative neural network model of feelings and emotions is proposed on the base of available data on their neuroscience and evolutionary biology nature, and on a neural network human memory model which admits distinct description of conscious and unconscious mental processes in a time dependent manner. As an example, proposed model is applied to quantitative description of the feeling of knowing.; Comment: A report presented at the IVth Kharkiv International Psychology Conference dedicated to 70th anniversary of psychology studies at Kharkiv University "Psychology in Contemporary World :Theory and Practice" held in Kharkiv, Ukraine, April 18-19, 2002, 5 pages, 2 Figures

‣ Insights Into Quantitative Biology: analysis of cellular adaptation

Agoni, Valentina
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/07/2013 Português
Relevância na Pesquisa
56.92071%
In the last years many powerful techniques have emerged to measure protein interactions as well as gene expression. Many progresses have been done since the introduction of these techniques but not toward quantitative analysis of data. In this paper we show how to study cellular adaptation and how to detect cellular subpopulations. Moreover we go deeper in analyzing signal transduction pathways dynamics.; Comment: 8 pages, 3 figures

‣ Is there any measurable benefit in publishing preprints in the arXiv section Quantitative Biology?

Aman, Valeria
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/11/2014 Português
Relevância na Pesquisa
67.14856%
A public preprint server such as arXiv allows authors to publish their manuscripts before submitting them to journals for peer review. It offers the chance to establish priority by making the results available upon completion. This article presents the arXiv section Quantitative Biology and investigates the advantages of preprint publications in terms of reception, which can be measured by means of citations. This paper focuses on the publication and citation delay, citation counts and the authors publishing their e-prints on arXiv. Moreover, the paper discusses the benefit for scientists as well as publishers. The results that are based on 12 selected journals show that submitting preprints to arXiv has become more common in the past few years, but the number of papers submitted to Quantitative Biology is still small and represents only a fraction of the total research output in biology. An immense advantage of arXiv is to overcome the long publication delay resulting from peer review. Although preprints are visible prior to the officially published articles, a significant citation advantage was only found for the Journal of Theoretical Biology.

‣ Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas

Grange, Pascal; Hawrylycz, Michael; Mitra, Partha P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/01/2013 Português
Relevância na Pesquisa
46.969155%
We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for download.; Comment: 25 pages, 8 figures, accepted in Quantitative Biology (2012) 0002

‣ Computer algebra in systems biology

Laubenbacher, Reinhard; Sturmfels, Bernd
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.002427%
Systems biology focuses on the study of entire biological systems rather than on their individual components. With the emergence of high-throughput data generation technologies for molecular biology and the development of advanced mathematical modeling techniques, this field promises to provide important new insights. At the same time, with the availability of increasingly powerful computers, computer algebra has developed into a useful tool for many applications. This article illustrates the use of computer algebra in systems biology by way of a well-known gene regulatory network, the Lac Operon in the bacterium E. coli.; Comment: to appear in American Mathematical Monthly

‣ Borges Dilemma, Fundamental Laws, and Systems Biology

Ao, P
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/08/2007 Português
Relevância na Pesquisa
47.14856%
I reason here that the known folk law in biology that there is no general law in biology because of exceptions is false. The (quantitative) systems biology offers the potential to solve the Borges Dilemma, by transcending it. There have already a plenty of indications on this trend.; Comment: 4 pages