https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/issue/feedLetters in Biomathematics2020-09-17T08:50:32-07:00Olcay Akmanbiomath@ilstu.eduOpen Journal Systems<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>https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/261The wisdom of a crowd of near-best fits2020-03-03T13:15:10-08:00Ellie Mainouejm6007@psu.eduGwen Spencergwenspencer@gmail.comDylan Shepardsondshepard@mtholyoke.eduRobert Doritrdorit@smith.edu<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>2020-03-03T09:53:33-08:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/223A Mathematical model of Malaria transmission dynamics with general incidence function and maturation delay in a periodic environment2020-03-06T12:35:49-08:00Bakary Traorétraore.baky@outlook.frOusmane Koutoukoutousman@gmail.comBoureima Sangarémazou1979@yahoo.fr<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>2020-03-06T12:35:18-08:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/207A Near-Optimal Control Method for Stochastic Boolean Networks2020-07-20T12:26:19-07:00Boris Aguilarboris.aguilar@systemsbiology.orgPan Fangpfang@tulane.eduReinhard Laubenbacherlaubenbacher@uchc.eduDavid Murrugarramurrugarra@uky.edu<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>2020-05-04T21:00:50-07:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/271Statistical decomposition of cumulative epidemiological curves into autochthonous and imported cases2020-09-17T08:48:09-07:00Brandon Lieberthalbrandon.lieberthal@maine.eduAiman Solimanasoliman@illinois.eduAllison Gardnerallison.gardner@maine.edu<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">As a pathogen spreads throughout a human population network, understanding the time span between the first reported infection case and the establishment of local transmission relies on the ability to decompose infection incidence into local and travel cases, depending on whether the infected individual was exposed to the pathogen in their location of residence or elsewhere. However, most case data reported to public health agencies do not distinguish between local and travel-associated cases, hampering analysis of the critical early stages of the epidemic spread. We introduce an algorithm, based on the shape of the cumulative incidence curve, to estimate the time a pathogen takes to become locally established, based on the pathogen’s transmission and recovery rates and the network connectivity of the human population. This algorithm can predict the onset of an epidemic without considering any future case data, making it useful for tracking epidemics as they occur.</p>2020-09-17T08:47:46-07:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/333A Discrete Age Structured Model of Hantavirus in a Rodent Reservoir in Paraguay2020-09-17T08:50:32-07:00Morganne Igoemigoe@utk.eduE. Joe Moranmoranjoe7296@gmail.comTheresa Sheetssheetst1@umbc.eduJeff DeSalujmdesalu@gmail.comColleen B. Jonssoncjonsson@uthsc.eduSuzanne Lenhartslenhart@tennessee.eduRobert D. Owenrowen@pla.net.pyMegan A. Rúamegan.rua@wright.edu<p>Many rodent-borne hantaviruses are zoonotic pathogens that can cause disease in humans through inhalation of aerosolized rodent excreta. To evaluate the prevalence of Jaborá virus (JABV) over time within its rodent reservoir, <em>Akodon montensis</em>, we formulated a mathematical model with multiple rodent age classes and unique infection class progression features. We then parameterized the model with data collected from a survey of JABV harbored by <em>Akodon montensis</em> in the Mbaracayú Reserve in Paraguay. Our model incorporates three types of infection over the lifetime of the rodent as well as a recovered class. A new feature of the model allows transition from the latent to the persistently-infected class. With a more complete age and infection structure, we are better able to identify the driving forces of epidemiology of hantaviruses in rodent populations.</p>2020-09-17T08:50:15-07:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/205Modeling 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 students2020-04-02T19:59:26-07:00Renee Dalerdale1@lsu.eduNaohiro Katokato@lsu.eduBill Wischusenewischu@lsu.edu<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>2020-02-28T11:53:25-08:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/21521st Century Reform Efforts in Undergraduate Quantitative Biology Education2020-05-04T20:46:57-07:00Carrie Diaz Eatoncdeaton@bates.eduM. Drew LaMarmdlama@wm.eduMaeve McCarthymmccarthy@murraystate.edu<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>2020-05-04T20:46:17-07:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/291Algebraic models, inverse problems, and pseudomonomials from biology2020-07-14T18:37:48-07:00Matthew Macauleymacaule@clemson.eduRaina RobevaRainaRobeva@rmc.edu<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">In contrast to the extensive use of linear algebra in biology, problems involving nonlinear polynomials—the field of <em>algebraic biology</em>—are more conspicuous. In this article, we highlight two biological problems where similar algebraic structures arise in very different contexts. Specifically, we will look at algebraic models of molecular networks, and combinatorial codes of place fields in neuroscience. Both of these topics involve inverse problems where the data is encoded with <em>pseudomonomials</em>, simple algebraic objects that do not seem to have been studied much on their own, but have gained considerable attention lately from their visibility in mathematical biology. Though this article is algebraic in nature, it is written for the general mathematician with a minimal algebra background assumed. It provides two distinctive features that have not yet appeared in the literature: (i) a survey of Boolean and logical modeling from a computational algebra perspective, and (ii) a unification of this topic with algebraic neuroscience by highlighting the role of pseudomonomials in both fields.</p>2020-07-12T10:59:46-07:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/251An Important Milestone Reached2020-01-17T13:38:01-08:00Olcay Akmanoakman@ilstu.edu2020-01-15T00:00:00-08:00Copyright (c) 2020 Letters in Biomathematicshttps://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/393Battling Epidemics & Disparity with Modeling2020-09-02T04:57:03-07:00Anuj Mubayianujmubayi@yahoo.comJeff Sullivanjeff.sullivan@precisionvh.comJason Shafrinjason.shafrin@precisionvh.comOliver Diazoliver.diaz@precisionvh.comAditi Ghoshghosha@uww.eduAnamika Mubayianamika.mubayi@gmail.comOlcay Akmanoakman@ilstue.duPhani Veerankiphani.veeranki@precisionvh.com<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Policymakers are under intense pressure to respond effectively to the ongoing COVID-19 situation. Epidemiological models, which have been helpful in many previous infectious diseases’ epidemics, have been inconsistent and often incorrect in predicting burden of COVID-19 outbreak. Modelers are struggling to identify and capture appropriate drivers of the current outbreak giving conflicting conclusions. COVID-19 is not only exerting unprecedented social pressure on the vulnerable population but also its patterns are getting impacted by existing and aggravating social problems. The present article stresses the role of this dual nature of the impact of COVID-19 and suggests modelers to incorporate challenges at the interface of COVID-19 preparedness and social epidemics such as homelessness and opioid use. There is an urgent need to encourage social distancing policies to protect people and prevent the spread of the virus, while ensuring that other social crises and vulnerable populations are not ignored.</p>2020-08-28T16:56:11-07:00Copyright (c) 2020 Letters in Biomathematics