All biology is computational biology

Methods in quantitative biology-from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression

The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying mathematical and computational methods. The central goals of q-bio are to (1) systematically propose quantitative hypotheses in the form of mathematical models, (2) demonstrate that these models faithfully capture a specific essence of a biological process, and (3) correctly forecast the dynamics of the process in new, and previously untested circumstances. Achieving these goals depends on accurate analysis and incorporating informative experimental data to constrain the set of potential mathematical representations. In this introductory tutorial, we provide an overview of the state of the field and introduce some of the computational methods most commonly used in q-bio. In particular, we examine experimental techniques in single-cell imaging, computational tools to process images and extract quantitative data, various mechanistic modeling approaches used to reproduce these quantitative data, and techniques for data-driven model inference and model-driven experiment design. All topics are presented in the context of additional online resources, including open-source Python notebooks and open-ended practice problems that comprise the technical content of the annual Undergraduate Quantitative Biology Summer School (UQ-Bio).

Twenty years in metalloprotein bioinformatics: A short history of a long journey

The study of the structure and function of metalloproteins is a central subject of inorganic biochemistry. Starting from the 2000s, computational methods have flanked experimental research by exploiting the ever-increasing computing power and the huge amount of data produced by omics technologies. In this article, we retrace the major advancements that brought bioinformatics from being of minor relevance to being an essential tool for today's inorganic biochemists, focusing on the contributions coming from the Magnetic Resonance Center (CERM) of Florence, where we have been developing for twenty years methods and resources to investigate metalloproteins with computational approaches.

On the Response of Proteinoid Ensembles to Fibonacci Sequences

This work investigates the integration of Fibonacci patterns and Golden Ratio principles into proteinoid-based systems, connecting fundamental mathematical concepts with contemporary biomimetic approaches. Proteinoids are thermal proteins that can self-assemble and have enzyme-like capabilities. They provide a distinct platform for biomimetic information processing. Our study examines the impact of integrating Fibonacci sequences and the Golden Ratio (ϕ = 1.618) into the design and synthesis of proteinoids on their structural organization and response characteristics. We developed two categories of stimuli: auditory signals generated using frequencies derived from the Fibonacci sequence, and electrical patterns that correspond to the proportions of the Golden Ratio. The proteinoid microsphere assemblies were subjected to these stimuli, and their electrical and structural responses were recorded and analyzed. The results indicate that proteinoid systems reveal unique reactivity to acoustic stimuli based on the Fibonacci sequence, exhibiting heightened sensitivity to particular combinations of frequencies and demonstrating nonlinear amplification effects. The proteinoid assemblies exhibited distinctive temporal dynamics and emergent oscillatory behaviors when exposed to voltage patterns inspired by the Golden Ratio, which were not detected with ordinary input signals. These findings provide opportunities for developing advanced bioinspired information transfer and security systems and might improve our understanding of information processing in early chemical systems.

The unusual suspect: A novel role for intermediate filament proteins in mitochondrial morphology

Mitochondrial dynamics is crucial for cellular homeostasis. However, not all proteins involved are known. Using a protein-protein interaction (PPI) approach, we identified ITPRIPL2 for involvement in mitochondrial dynamics. ITPRIPL2 co-localizes with intermediate filament protein vimentin, supported by protein simulations. ITPRIPL2 knockdown reveals mitochondrial elongation, disrupts vimentin processing, intermediate filament formation, and alters vimentin-related pathways. Interestingly, vimentin knockdown also leads to mitochondrial elongation. These findings highlight ITPRIPL2 as vimentin-associated protein essential for intermediate filament structure and suggest a role for intermediate filaments in mitochondrial morphology. Our study demonstrates that PPI analysis is a powerful approach for identifying novel mitochondrial dynamics proteins.

Improving bioinformatics software quality through teamwork

SUMMARY

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

AVAILABILITY AND IMPLEMENTATION

Additional information and guidance on how to get started is available at https://ferenckata.github.io/ImprovingSoftwareTogether.github.io/.

A new era of synthetic biology-microbial community design

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.

Biology's transformation: from observation through experiment to computation

Summary

We explore the nuanced temporal and epistemological distinctions among natural sciences, particularly the contrasting treatment of time and the interplay between theory and experimentation. Physics, an exemplar of mature science, relies on theoretical models for predictability and simulations. In contrast, biology, traditionally experimental, is witnessing a computational surge, with data analytics and simulations reshaping its research paradigms. Despite these strides, a unified theoretical framework in biology remains elusive. We propose that contemporary global challenges might usher in a renewed emphasis, presenting an opportunity for the establishment of a novel theoretical underpinning for the life sciences.

Availability and implementation

https://github.com/ouzounis/CLS-emerges Data in Json format, Images in PNG format.

Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

Ten simple rules for managing laboratory information

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.

A hypothetical model of multi-layered cost-effective wastewater treatment plant integrating microbial fuel cell and nanofiltration technology: A comprehensive review on wastewater treatment and sustainable remediation

Wastewater management has emerged as an uprising concern that demands immediate attention from environmentalists worldwide. Indiscriminate and irrational release of industrial and poultry wastes, sewage, pharmaceuticals, mining, pesticides, fertilizers, dyes and radioactive wastes, contribute immensely to water pollution. This has led to the aggravation of critical health concerns as evident from the uprising trends of antimicrobial resistance, and the presence of xenobiotics and pollutant traces in humans and animals due to the process of biomagnification. Therefore, the development of reliable, affordable and sustainable technologies for the supply of fresh water is the need of the hour. Conventional wastewater treatment often involves physical, chemical, and biological processes to remove solids from the effluent, including colloids, organic matter, nutrients, and soluble pollutants (metals, organics). Synthetic biology has been explored in recent years, incorporating both biological and engineering concepts to refine existing wastewater treatment technologies. In addition to outlining the benefits and drawbacks of the current technologies, this review addresses novel wastewater treatment techniques, especially those using dedicated rational design and engineering of organisms and their constituent parts. Furthermore, the review hypothesizes designing a multi-bedded wastewater treatment plant that is highly cost-efficient, sustainable and requires easy installation and handling. The novel setup envisages removing all the major wastewater pollutants, providing water fit for household, irrigation and storage purposes.

Computational biology: Role and scope in taming antimicrobial resistance

BACKGROUND

Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of computational biology there are tools and techniques available to address and solve the disease management issues in the field of clinical microbiology. The sequencing techniques, structural biology and machine learning can be implemented collectively to tackle infectious diseases e.g. for the diagnosis, epidemiological typing, pathotyping, antimicrobial resistance detection as well as the discovery of novel drugs and vaccine biomarkers.

OBJECTIVES

The present review is a narrative review representing a comprehensive literature-based assessment regarding the use of whole genome sequencing, structural biology and machine learning for the diagnosis, molecular typing and antibacterial drug discovery.

CONTENT

Here, we seek to present an overview of molecular and structural basis of resistance to antibiotics, while focusing on the recent bioinformatics approaches in whole genome sequencing and structural biology. The application of next generation sequencing in management of bacterial infections focusing on investigation of microbial population diversity, genotypic resistance testing and scope for the identification of targets for novel drug and vaccine candidates, has been addressed along with the use of structural biophysics and artificial intelligence.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Entropy and Variability: A Second Opinion by Deep Learning

BACKGROUND

Analysis of the distribution of amino acid types found at equivalent positions in multiple sequence alignments has found applications in human genetics, protein engineering, drug design, protein structure prediction, and many other fields. These analyses tend to revolve around measures of the distribution of the twenty amino acid types found at evolutionary equivalent positions: the columns in multiple sequence alignments. Commonly used measures are variability, average hydrophobicity, or Shannon entropy. One of these techniques, called entropy-variability analysis, as the name already suggests, reduces the distribution of observed residue types in one column to two numbers: the Shannon entropy and the variability as defined by the number of residue types observed.

RESULTS

We applied a deep learning, unsupervised feature extraction method to analyse the multiple sequence alignments of all human proteins. An auto-encoder neural architecture was trained on 27,835 multiple sequence alignments for human proteins to obtain the two features that best describe the seven million variability patterns. These two unsupervised learned features strongly resemble entropy and variability, indicating that these are the projections that retain most information when reducing the dimensionality of the information hidden in columns in multiple sequence alignments.

Biology and medicine in the landscape of quantum advantages

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

Inspirations of Biomimetic Affinity Ligands: A Review

Affinity chromatography is a well-known method dependent on molecular recognition and is used to purify biomolecules by mimicking the specific interactions between the biomolecules and their substrates. Enzyme substrates, cofactors, antigens, and inhibitors are generally utilized as bioligands in affinity chromatography. However, their cost, instability, and leakage problems are the main drawbacks of these bioligands. Biomimetic affinity ligands can recognize their target molecules with high selectivity. Their cost-effectiveness and chemical and biological stabilities make these antibody analogs favorable candidates for affinity chromatography applications. Biomimetics applies to nature and aims to develop nanodevices, processes, and nanomaterials. Today, biomimetics provides a design approach to the biomimetic affinity ligands with the aid of computational methods, rational design, and other approaches to meet the requirements of the bioligands and improve the downstream process. This review highlighted the recent trends in designing biomimetic affinity ligands and summarized their binding interactions with the target molecules with computational approaches.

Biology Students' Math and Computer Science Task Values Are Closely Linked

Quantitative and computational skills are required of 21st-century biologists. While biology student abilities and attitudes toward math have been studied extensively, less is known about corresponding attitudes toward computer science (CS). It is important to understand how students perceive math and CS subjects and whether those perceptions are linked or operate contradictorily to determine instructional best practices. This study 1) determined biology students' perceptions of math and CS in biological contexts, 2) measured the linkage of those perceptions, and 3) examined additional factors affecting attitudes. Students (N = 272) were surveyed using the original and a CS-adapted version of the Math-Biology Values Instrument to determine interest, perceived utility, and perceived costs toward math and CS in biological contexts. Mixed-effects models were used to determine correlations between task values and investigate effects of exposure to topics and demographic factors. Math and CS values exhibited positive correlations, but utility and cost were more negative for CS, possibly due to less exposure to CS before college, and overall attitudes were influenced by CS background and gender. Given these findings, we make educational recommendations for CS and math exposure early, often, and embedded in the biology curriculum.

From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.

Proteome-wide prediction and analysis of the Cryptosporidium parvum protein-protein interaction network through integrative methods

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By combining a sequence embedding technique (i.e., Doc2Vec) and a di-peptide composition representation to convert protein sequences into feature vectors, we proposed an RF classifier trained on the Plasmodium falciparum dataset for predicting Cryptosporidium parvum PPIs.

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A high-confidence Cryptosporidium parvum PPI network was identified by conjoining interolog mapping, domain-domain interaction-based inference, and the RF classifier.

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Some detected hub proteins and functional modules provided clues for an in-depth biological understanding of Cryptosporidium parvum.

As one of the most studied Apicomplexan parasite Cryptosporidium, Cryptosporidium parvum (C. parvum) causes worldwide serious diarrhea disease cryptosporidiosis, which can be deadly to immunodeficiency individuals, newly born children, and animals. Proteome-wide identification of protein–protein interactions (PPIs) has proven valuable in the systematic understanding of the genome-phenome relationship. However, the PPIs of C. parvum are largely unknown because of the limited experimental studies carried out. Therefore, we took full advantage of three bioinformatics methods, i.e., interolog mapping (IM), domain-domain interaction (DDI)-based inference, and machine learning (ML) method, to jointly predict PPIs of C. parvum. Due to the lack of experimental PPIs of C. parvum, we used the PPI data of Plasmodium falciparum (P. falciparum), which owned the largest number of PPIs in Apicomplexa, to train an ML model to infer C. parvum PPIs. We utilized consistent results of these three methods as the predicted high-confidence PPI network, which contains 4,578 PPIs covering 554 proteins. To further explore the biological significance of the constructed PPI network, we also conducted essential network and protein functional analysis, mainly focusing on hub proteins and functional modules. We anticipate the constructed PPI network can become an important data resource to accelerate the functional genomics studies of C. parvum as well as offer new hints to the target discovery in developing drugs/vaccines.

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps

Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell-cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.

GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

Unraveling Aβ-Mediated Multi-Pathway Calcium Dynamics in Astrocytes: Implications for Alzheimer's Disease Treatment From Simulations

The accumulation of amyloid β peptide (Aβ) in the brain is hypothesized to be the major factor driving Alzheimer's disease (AD) pathogenesis. Mounting evidence suggests that astrocytes are the primary target of Aβ neurotoxicity. Aβ is known to interfere with multiple calcium fluxes, thus disrupting the calcium homeostasis regulation of astrocytes, which are likely to produce calcium oscillations. Ca²⁺ dyshomeostasis has been observed to precede the appearance of clinical symptoms of AD; however, it is experimentally very difficult to investigate the interactions of many mechanisms. Given that Ca²⁺ disruption is ubiquitously involved in AD progression, it is likely that focusing on Ca²⁺ dysregulation may serve as a potential therapeutic approach to preventing or treating AD, while current hypotheses concerning AD have so far failed to yield curable therapies. For this purpose, we derive and investigate a concise mathematical model for Aβ-mediated multi-pathway astrocytic intracellular Ca²⁺ dynamics. This model accounts for how Aβ affects various fluxes contributions through voltage-gated calcium channels, Aβ-formed channels and ryanodine receptors. Bifurcation analysis of Aβ level, which reflected the corresponding progression of the disease, revealed that Aβ significantly induced the increasing [Ca²⁺] _i and frequency of calcium oscillations. The influence of inositol 1,4,5-trisphosphate production (IP₃) is also investigated in the presence of Aβ as well as the impact of changes in resting membrane potential. In turn, the Ca²⁺ flux can be considerably changed by exerting specific interventions, such as ion channel blockers or receptor antagonists. By doing so, a "combination therapy" targeting multiple pathways simultaneously has finally been demonstrated to be more effective. This study helps to better understand the effect of Aβ, and our findings provide new insight into the treatment of AD.

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Accelerating COVID-19 Research Using Molecular Dynamics Simulation

The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.

The early bird gets the return: The benefits of publishing your data sooner

The benefits of publishing your data sooner versus later in Ecology and Evolution.

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.

Prefrontal Cortex Metabolome Is Modified by Opioids, Anesthesia, and Sleep

Obtundation of wakefulness caused by opioids and loss of wakefulness caused by anesthetics and sleep significantly alter concentrations of molecules comprising the prefrontal cortex (PFC) metabolome. Quantifying state-selective changes in the PFC metabolome is essential for advancing functional metabolomics. Diverse functions of the PFC suggest the PFC metabolome as a potential therapeutic entry point for countermeasures to state-selective autonomic dysfunction.

Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.

Data science in cell imaging

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

The roles of code in biology

The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a "sweet spot" in the taxonomy-a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the "inverse Conway maneuver" to shape a research team so as to allow dedicated software engineers to interface with researchers working in this "sweet spot." We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.

Online but not remote: Adapting field-based ecology laboratories for online learning

Teaching ecology effectively and experientially has become more challenging for at least two reasons today. Most experiences of our students are urban, and we now face the near immediate and continuing need to deliver courses (either partially or wholly) online because of COVID-19. Therefore, providing a learning experience that connects students to their environment within an ecological framework remains crucial and perhaps therapeutic to mental health. Here, we describe how prior to the pandemic we adapted our field-based laboratories to include data collection, analysis, and interpretation, along with the development of a citizen-science approach for online delivery. This design is simple to implement, does not require extensive work, and maintains the veracity of original learning outcomes. Collaboration online following field data collection in ecology courses within the context of cities offers further options to adapt to student experience levels, resource availability, and accessibility, as well as bringing instructors and students together to build an open well-curated data set that can be used in ecology courses where no laboratories are available. Finally, it promotes an open collaboration among ecology instructors that can drive lasting conversations about ecology curriculum.

From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences

Decades of reductionist approaches in biology have achieved spectacular progress, but the proliferation of subdisciplines, each with its own technical and social practices regarding data, impedes the growth of the multidisciplinary and interdisciplinary approaches now needed to address pressing societal challenges. Data integration is key to a reintegrated biology able to address global issues such as climate change, biodiversity loss, and sustainable ecosystem management. We identify major challenges to data integration and present a vision for a "Data as a Service"-oriented architecture to promote reuse of data for discovery. The proposed architecture includes standards development, new tools and services, and strategies for career-development and sustainability.

Multi-Omics Data Integration in Extracellular Vesicle Biology-Utopia or Future Reality?

Extracellular vesicles (EVs) are membranous structures derived from the endosomal system or generated by plasma membrane shedding. Due to their composition of DNA, RNA, proteins, and lipids, EVs have garnered a lot of attention as an essential mechanism of cell-to-cell communication, with various implications in physiological and pathological processes. EVs are not only a highly heterogeneous population by means of size and biogenesis, but they are also a source of diverse, functionally rich biomolecules. Recent advances in high-throughput processing of biological samples have facilitated the development of databases comprised of characteristic genomic, transcriptomic, proteomic, metabolomic, and lipidomic profiles for EV cargo. Despite the in-depth approach used to map functional molecules in EV-mediated cellular cross-talk, few integrative methods have been applied to analyze the molecular interplay in these targeted delivery systems. New perspectives arise from the field of systems biology, where accounting for heterogeneity may lead to finding patterns in an apparently random pool of data. In this review, we map the biological and methodological causes of heterogeneity in EV multi-omics data and present current applications or possible statistical methods for integrating such data while keeping track of the current bottlenecks in the field.

Online conferences for better learning

Conferences provide an invaluable set of opportunities for professional development. Online, virtual, and distributed conferences do not necessarily mean less opportunity for growth and innovation in science but varied and novel options for communicating the scientific process. Open science and many existing tools are in place in the practice of contemporary ecology and evolution to provide latitude for a much broader scope of sharing and thus learning from conferences. A brief overview of the science supporting online conferences and a highlight of some of the open science concepts in ecology and evolution are provided here to enable better learning through better planning for online conferences.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability

Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips—and a web tool, PubReCheck—guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors’ work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck.

Your published research is already being processed with automated tools, and text mining will become more common; this Community Page article describes how you can help these tools process your work more accurately, including a web tool, PubReCheck.

A checklist for choosing between R packages in ecology and evolution

The open source and free programming language R is a phenomenal mechanism to address a multiplicity of challenges in ecology and evolution. It is also a complex ecosystem because of the diversity of solutions available to the analyst.Packages for R enhance and specialize the capacity to explore both niche data/experiments and more common needs. However, the paradox of choice or how we select between many seemingly similar options can be overwhelming and lead to different potential outcomes.There is extensive choice in ecology and evolution between packages for both fundamental statistics and for more specialized domain-level analyses.Here, we provide a checklist to inform these decisions based on the principles of resilience, need, and integration with scientific workflows for evidence.It is important to explore choices in any analytical coding environment-not just R-for solutions to challenges in ecology and evolution, and document this process because it advances reproducible science, promotes a deeper understand of the scientific evidence, and ensures that the outcomes are correct, representative, and robust.

Telescope: an interactive tool for managing large-scale analysis from mobile devices

BACKGROUND

In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone.

RESULTS

To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood.

CONCLUSIONS

Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.

Challenges and recommendations to improve the installability and archival stability of omics computational tools

Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through uniform resource locators (URLs) published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed "easy to install," and 28% of the tools failed to be installed at all because of problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software.

Repairing Boolean logical models from time-series data using Answer Set Programming

Background

Boolean models of biological signalling-regulatory networks are increasingly used to formally describe and understand complex biological processes. These models may become inconsistent as new data become available and need to be repaired. In the past, the focus has been shed on the inference of (classes of) models given an interaction network and time-series data sets. However, repair of existing models against new data is still in its infancy, where the process is still manually performed and therefore slow and prone to errors.

Results

In this work, we propose a method with an associated tool to suggest repairs over inconsistent Boolean models, based on a set of atomic repair operations. Answer Set Programming is used to encode the minimal repair problem as a combinatorial optimization problem. In particular, given an inconsistent model, the tool provides the minimal repairs that render the model capable of generating dynamics coherent with a (set of) time-series data set(s), considering either a synchronous or an asynchronous updating scheme.

Conclusions

The method was validated using known biological models from different species, as well as synthetic models obtained from randomly generated networks. We discuss the method’s limitations regarding each of the updating schemes and the considered minimization algorithm.

Electronic supplementary material

The online version of this article (10.1186/s13015-019-0145-8) contains supplementary material, which is available to authorized users.

Improving the usability and archival stability of bioinformatics software

Implementation of bioinformatics software involves numerous unique challenges; a rigorous standardized approach is needed to examine software tools prior to their publication.

Designing and running an advanced Bioinformatics and genome analyses course in Tunisia

Genome data, with underlying new knowledge, are accumulating at exponential rate thanks to ever-improving sequencing technologies and the parallel development of dedicated efficient Bioinformatics methods and tools. Advanced Education in Bioinformatics and Genome Analyses is to a large extent not accessible to students in developing countries where endeavors to set up Bioinformatics courses concern most often only basic levels. Here, we report a pioneering pilot experience concerning the design and implementation, from scratch, of a three-months advanced and extensive course in Bioinformatics and Genome Analyses in the Institut Pasteur de Tunis. Most significantly the outcome of the course was upgrading the participants’ skills in Bioinformatics and Genome Analyses to recognized international standards. Here we detail the different steps involved in the implementation of this course as well as the topics covered in the program. The description of this pilot experience might be helpful for the implementation of other similar educational projects, notably in developing countries, aiming to go beyond basics and providing young researchers with high-level skills.

Sharing and reusing cell image data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.