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Increasingly, big data, coding, and quantitative methods contribute to contemporary ecological and evolutionary endeavours. This is not in opposition to effective ideation nor does it play to the false dichotomy of theory versus data. Computational expeditions with data, models, simulations or any other number of approaches both expand the toolkit of science and promote more structured reasoning. The implications of computational biology integrated with scientific pursuits such as experiments and theory development include the following positive outcomes: enhanced open science, better reproducibility, data literacy, author inclusivity, social good, and novel ideation opportunities. We face a climate apocalypse and unprecedented ecological challenges of collapsing ecosystem functions. Computation coupled with ideation is one mechanism to align the hearts and heads of scientists and decision makers alike.

10 páginas, 3 figuras, 1 tabla.-- International Conference On Computational Science, ICCS 2015, Computational Science at the Gates of Nature.-- Under a Creative Commons license ; Metaheuristics are gaining increased attention as efficient solvers for hard global optimization problems arising in bioinformatics and computational systems biology. Scatter Search (SS) is one of the recent outstanding algorithms in that class. However, its application to very hard problems, like those considering parameter estimation in dynamic models of systems biology, still results in excessive computation times. In order to reduce the computational cost of the SS and improve its success, several research efforts have been made to propose different variants of the algorithm, including parallel approaches. This work presents an asynchronous Cooperative enhanced Scatter Search (aCeSS) based on the parallel execution of different enhanced Scatter Search threads and the cooperation between them. The main features of the proposed solution are: low overhead in the cooperation step, by means of an asynchronous protocol to exchange information between processes; more effectiveness of the cooperation step, since the exchange of information is driven by quality of the solution obtained in each process, rather than by a time elapsed; optimal use of available resources, thanks to a complete distributed approach that avoids idle processes at any moment. Several challenging parameter estimation problems from the domain of computational systems biology are used to assess the efficiency of the proposal and evaluate its scalability in a parallel environment ; This research received financial support from the Spanish Ministerio de Econom´ıa y Competitividad (and the FEDER) through the projects DPI2011-28112-C04-03, DPI2011-28112-C04- 04, and TIN2013-42148-P, from the CSIC intramural project "BioREDES" (PIE-201170E018) and from the Galician Government under the Consolidation Program of Competitive Research Units (Network ref. R2014/041 and competitive reference groups GRC2013/055). D. R. Penas acknowledges financial support from the MICINN-FPI programme ; Peer reviewed

This unique volume surveys state-of-the-art research on statistical methods in molecular and systems biology, with contributions from leading experts in the field. Each chapter discusses theoretical aspects, applications to biological problems, and possible future developments. Topics and features: presents the use of thermodynamic models to analyze gene regulatory mechanisms; reviews major algorithms for RNA secondary structure prediction; discusses developments in the area of oligo arrays; examines the application of models of stochastic processes in nonequilibrium thermodynamics and biologi.

Computational biology is poised to advance precision medicine with machine learning
Today, scientists are attempting to model whole cells using computational biology, building virtual cells that capture the dynamics of living. In the post-genomic era, vast quantities of data describing the parts of living cells, both normally functioning and diseased, have been amassed. Understanding how cells behave has been the goal of reductionist science, and many of the principles are well understood. In elucidating the details, complexity has been revealed to the degree that scientists have limited capacity to understand the systems underlying the biological networks. This is to say, cellular behavior is difficult to predict.

32 páginas, 12 figuras, 6 tablas.-- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited ; [Background] We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs) ; [Methods] We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. ; [Results] We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores) ; [Conclusions] These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling ; This research received financial support from the Spanish Ministerio de Economía y Competitividad (and the FEDER) through projects "SYNBIOFACTORY" (DPI2014-55276-C5-2-R) and TIN2016-75845-P (AEI/FEDER, UE), and from the Galician Government under the Consolidation Program of Competitive Research Units (Network ref. R2016/045 and competitive reference groups GRC2013/055). DRP acknowledges financial support from the MICINN-FPI programme ; Peer reviewed

The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models--integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools

In this paper, we present a meeting report for the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast. We describe the organization of the summer school, its underlying concept and student feedback we received after the completion of the summer school.

Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Python and the Surrounding Software Ecology -- Installing the required basic software with Anaconda -- Getting ready -- How to do it... -- There's more... -- Installing the required software with Docker -- Getting ready -- How to do it... -- See also -- Interfacing with R via rpy2 -- Getting ready -- How to do it... -- There's more... -- See also -- Performing R magic with Jupyter -- Getting ready -- How to do it... -- There's more... -- See also -- Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib -- Using pandas to process vaccine-adverse events -- Getting ready -- How to do it... -- There's more... -- See also -- Dealing with the pitfalls of joining pandas DataFrames -- Getting ready -- How to do it... -- There's more... -- Reducing the memory usage of pandas DataFrames -- Getting ready -- How to do it… -- See also -- Accelerating pandas processing with Apache Arrow -- Getting ready -- How to do it... -- There's more... -- Understanding NumPy as the engine behind Python data science and bioinformatics -- Getting ready -- How to do it… -- See also -- Introducing Matplotlib for chart generation -- Getting ready -- How to do it... -- There's more... -- See also -- Chapter 3: Next-Generation Sequencing -- Accessing GenBank and moving around NCBI databases -- Getting ready -- How to do it... -- There's more... -- See also -- Performing basic sequence analysis -- Getting ready -- How to do it... -- There's more... -- See also -- Working with modern sequence formats -- Getting ready -- How to do it... -- There's more... -- See also -- Working with alignment data -- Getting ready -- How to do it... -- There's more... -- See also -- Extracting data from VCF files -- Getting ready -- How to do it... -- There's more... -- See also.

Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Some General Remarks on Mathematical Modeling -- Bibliographic Remarks -- PART 1. BASIC POPULATION GROWTH MODELS -- Chapter 2. Birth, Death, and Migration -- 2.1 The Fundamental Balance Equation of Population Dynamics -- 2.2 Birth Date Dependent Life Expectancies -- 2.3 The Probability of Lifetime Emigration -- Chapter 3. Unconstrained Population Growth for Single Species -- 3.1 Closed Populations -- 3.1.1 The Average Intrinsic Growth Rate for Periodic Environments -- 3.1.2 The Average Intrinsic Growth Rate for Nonperiodic Environments -- 3.2 Open Populations -- 3.2.1 Nonzero Average Intrinsic Growth Rate -- 3.2.2 Zero Average Intrinsic Growth Rate -- Chapter 4. Von Bertalanffy Growth of Body Size -- Chapter 5. Classic Models of Density-Dependent Population Growth for 37 Single Species -- 5.1 The Bernoulli and the Verhulst Equations -- 5.2 The Beverton-Holt and Smith Differential Equation -- 5.2.1 Derivation from a Resource-Consumer Model -- 5.2.2 Derivation from Cannibalism of Juveniles by Adults -- 5.3 The Ricker Differential Equation -- 5.4 The Gompertz Equation -- 5.5 A First Comparison of the Various Equations -- Chapter 6. Sigmoid Growth -- 6.1 General Conditions for Sigmoid Growth -- 6.2 Fitting Sigmoid Population Data -- Chapter 7. The Allee Effect -- 7.1 First Model Derivation: Search for a Mate -- 7.2 Second Model Derivation: Impact of a Satiating Generalist Predator -- 7.3 Model Analysis -- Chapter 8. Nonautonomous Population Growth:Asymptotic Equality of 75 Population Sizes -- Chapter 9. Discrete-Time Single-Species Models -- 9.1 The Discrete Analog of the Verhulst (Logistic) and the Bernoulli Equation: the Beverton-Holt Difference Equation and Its Generalization -- 9.2 The Ricker Difference Equation -- 9.3 Some Analytic Results for Scalar Difference Equations