Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib <p><strong><em>Letters in Biomathematics</em></strong> is an open-access journal that lies at the interface of mathematics, statistics, biology, ecology, and the life sciences. The journal publishes Research, Education, and Review articles related to biological, ecological and environmental settings in a very broad sense, as well as other related topic fields.</p> en-US biomath@ilstu.edu (Olcay Akman) rcbunge@ilstu.edu (Ryan Bunge) Wed, 01 Jan 2020 00:00:00 -0800 OJS http://blogs.law.harvard.edu/tech/rss 60 The wisdom of a crowd of near-best fits https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/261 <p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Antibiotic-resistant tuberculosis (TB) strains pose a major challenge to TB eradication. Existing US epidemiological models have not fully incorporated the impact of antibiotic-resistance. To develop a more realistic model of US TB dynamics, we formulated a compartmental model integrating single- and multi-drug resistance. We fit twenty-seven parameters to twenty-two years of historical data using a genetic algorithm to minimize a non-differentiable error function. Since counts for several compartments are not available, many parameter combinations achieve very low error. We demonstrate that a crowd of near-best fits can provide compelling new evidence about the ranges of key parameters. While available data is sparse and insufficient to produce point estimates, our crowd of near-best fits computes remarkably consistent predictions about TB prevalence. We believe that our crowd-based approach is applicable to a common problem in mathematical biological research, namely situations where data are sparse and reliable point estimates cannot be directly obtained.</p> Ellie Mainou, Gwen Spencer, Dylan Shepardson, Robert Dorit Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/261 Tue, 03 Mar 2020 09:53:33 -0800 A Mathematical model of Malaria transmission dynamics with general incidence function and maturation delay in a periodic environment https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/223 <p>In this paper, we investigate a mathematical model of malaria transmission dynamics with maturation delay of a vector population in a periodic environment. The incidence rate between vector and human hosts is modeled by a general nonlinear incidence function which satisfies a set of conditions. Thus, the model is formulated as a system of retarded functional differential equations. Furthermore, through dynamical systems theory, we rigorously analyze the global behavior of the model. Therefore, we prove that the basic reproduction number of the model denoted by <em>R</em><sub>0</sub> is the threshold between the uniform persistence and the extinction of malaria virus transmission. More precisely, we show that if <em>R</em><sub>0</sub> is less than unity, then the disease-free periodic solution is globally asymptotically stable. Otherwise, the system exhibits at least one positive periodic solution if <em>R</em><sub>0</sub> is greater than unity. Finally, we perform some numerical simulations to illustrate our mathematical results and to analyze the impact of the delay on the disease transmission.</p> Bakary Traoré, Ousmane Koutou, Boureima Sangaré Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/223 Fri, 06 Mar 2020 12:35:18 -0800 A Near-Optimal Control Method for Stochastic Boolean Networks https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/207 <p class="pagecontents">One of the ultimate goals of computational biology and bioinformatics is to develop control strategies to find efficient medical treatments. One step towards this goal is to develop methods for changing the state condition of a cell into a new desirable state. Using a stochastic modeling framework generalized from Boolean Networks, we propose a computationally efficient method that determines sequential combinations of network perturbations, that induce the transition of a cell towards a new predefined state. The method requires a set of possible control actions as input, every element of this set represents the silencing of a gene or a disruption of the interaction between two molecules. An optimal control policy defined as the best intervention at each state of the system, can be obtained using theory of Markov decision processes. However, these algorithms are computationally prohibitive for models of tens of nodes. The proposed method generates a sequence of actions that approximates the optimal control policy with a computational efficiency that does not depend on the size of the state space of the system. The methods are validated by using published models where control targets have been identified. Our code in C++ is publicly available through GitHub at https://github.com/boaguilar/SDDScontrol.</p> Boris Aguilar, Pan Fang, Reinhard Laubenbacher, David Murrugarra Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/207 Mon, 04 May 2020 21:00:50 -0700 Modeling and analysis of the firefly luciferase reaction and the G-protein coupled receptor signaling pathway with ordinary differential equations increases self confidence in mathematical cell biology for novice graduate students https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/205 <p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">The push for mathematics and computation in biology is greater than ever before. However, perceptions among biology students on the difficulty of mathematics and programming are a barrier to their implementation. A teaching module introducing quantitative skills to biology students is needed. We implemented modeling of firefly luciferase and G-protein coupled receptor signaling with ordinary differential equations in a course for novice graduate students. We assessed whether the course helped the students increase self confidence in application of mathematics in biology. Two concept inventories tracked learning gains in both general and cell biology knowledge. Pre- and post-semester surveys quantified changes in student confidence and their opinions of the usefulness of these techniques in cell biology. We found that the modeling and analysis activities appeared to improve self confidence in and appreciation of quantitative mathematical biology techniques. We describe our assessment methods to determine the suitability of our module.</p> Renee Dale, Naohiro Kato, Bill Wischusen Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/205 Fri, 28 Feb 2020 11:53:25 -0800 21st Century Reform Efforts in Undergraduate Quantitative Biology Education https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/215 <p>In the United States, there are multiple reports from both mathematics and biology communities that address the quantitative preparation of undergraduate life science students. Many of them make broad recommendations for the revision of life science curriculum to incorporate more quantitative techniques. Here, we review initiatives and progress in the United States on the state of quantitative biology education in the context of the mathematics education, biology research frontiers, and the funding system and other sources of support for systemic change to meet new demands.</p> Carrie Diaz Eaton, M. Drew LaMar, Maeve McCarthy Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/215 Mon, 04 May 2020 20:46:17 -0700 An Important Milestone Reached https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/251 Olcay Akman Copyright (c) 2020 Letters in Biomathematics https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/251 Wed, 15 Jan 2020 00:00:00 -0800