Challenges in cybersecurity: Lessons from biological defense systems

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the 'self', are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms.

Gender-based homophily in collaborations across a heterogeneous scholarly landscape

In this article, we investigate the role of gender in collaboration patterns by analyzing gender-based homophily-the tendency for researchers to co-author with individuals of the same gender. We develop and apply novel methodology to the corpus of JSTOR articles, a broad scholarly landscape, which we analyze at various levels of granularity. Most notably, for a precise analysis of gender homophily, we develop methodology which explicitly accounts for the fact that the data comprises heterogeneous intellectual communities and that not all authorships are exchangeable. In particular, we distinguish three phenomena which may affect the distribution of observed gender homophily in collaborations: a structural component that is due to demographics and non-gendered authorship norms of a scholarly community, a compositional component which is driven by varying gender representation across sub-disciplines and time, and a behavioral component which we define as the remainder of observed gender homophily after its structural and compositional components have been taken into account. Using minimal modeling assumptions, the methodology we develop allows us to test for behavioral homophily. We find that statistically significant behavioral homophily can be detected across the JSTOR corpus and show that this finding is robust to missing gender indicators in our data. In a secondary analysis, we show that the proportion of women representation in a field is positively associated with the probability of finding statistically significant behavioral homophily.

Model-driven mitigation measures for reopening schools during the COVID-19 pandemic

Reopening schools is an urgent priority as the COVID-19 pandemic drags on. To explore the risks associated with returning to in-person learning and the value of mitigation measures, we developed stochastic, network-based models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in primary and secondary schools. We find that a number of mitigation measures, alone or in concert, may reduce risk to acceptable levels. Student cohorting, in which students are divided into two separate populations that attend in-person classes on alternating schedules, can reduce both the likelihood and the size of outbreaks. Proactive testing of teachers and staff can help catch introductions early, before they spread widely through the school. In secondary schools, where the students are more susceptible to infection and have different patterns of social interaction, control is more difficult. Especially in these settings, planners should also consider testing students once or twice weekly. Vaccinating teachers and staff protects these individuals and may have a protective effect on students as well. Other mitigations, including mask wearing, social distancing, and increased ventilation, remain a crucial component of any reopening plan.

Stewardship of global collective behavior

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a "crisis discipline" just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

Misinformation in and about science

Humans learn about the world by collectively acquiring information, filtering it, and sharing what we know. Misinformation undermines this process. The repercussions are extensive. Without reliable and accurate sources of information, we cannot hope to halt climate change, make reasoned democratic decisions, or control a global pandemic. Most analyses of misinformation focus on popular and social media, but the scientific enterprise faces a parallel set of problems-from hype and hyperbole to publication bias and citation misdirection, predatory publishing, and filter bubbles. In this perspective, we highlight these parallels and discuss future research directions and interventions.

Signals without teleology

"Signals" are a conceptual apparatus in many scientific disciplines. Biologists inquire about the evolution of signals, economists talk about the signaling function of purchases and prices, and philosophers discuss the conditions under which signals acquire meaning. However, less attention has been paid to what is a signal. Most existing accounts are teleological in some way. This paper provides a definition of signals that avoids reference to form or purpose. Along the way we introduce novel notions of "information revealing" and "information concealing" moves in games. In the end, our account offers an alternative to teleological accounts of communication.

Contest models highlight inherent inefficiencies of scientific funding competitions

Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder to screen for the most promising research ideas. Consequently, some of the funding program's impact on science is squandered because applying researchers must spend time writing proposals instead of doing science. To what extent does the community's aggregate investment in proposal preparation negate the scientific impact of the funding program? Are there alternative mechanisms for awarding funds that advance science more efficiently? We use the economic theory of contests to analyze how efficiently grant proposal competitions advance science, and compare them with recently proposed, partially randomized alternatives such as lotteries. We find that the effort researchers waste in writing proposals may be comparable to the total scientific value of the research that the funding supports, especially when only a few proposals can be funded. Moreover, when professional pressures motivate investigators to seek funding for reasons that extend beyond the value of the proposed science (e.g., promotion, prestige), the entire program can actually hamper scientific progress when the number of awards is small. We suggest that lost efficiency may be restored either by partial lotteries for funding or by funding researchers based on past scientific success instead of proposals for future work.

Scientists waste substantial time writing grant proposals, potentially squandering much of the scientific value of funding programs. This Meta-Research Article shows that, unfortunately, grant-proposal competitions are inevitably inefficient when the number of awards is small, but efficiency can be restored by awarding funds through a modified lottery, or by weighting past research success more heavily in funding decisions.

The grant proposal system compels researchers to devote substantial time to writing proposals that could have instead been used to do science. Here, we use the economic theory of contests to show that as fewer grants are funded, the value of the science that researchers forgo while preparing proposals can approach or exceed the value of the science that the funding program supports. As a result, much of the scientific impact of the funding program is squandered. Unfortunately, increased waste and reduced efficiency is inevitable in a grant proposal competition when the number of awards is small. How can scarce funds be allocated efficiently, then? As one alternative, we show that a partial lottery that selects proposals for funding randomly from among those that pass a qualifying standard can restore lost efficiency by reducing investigators' incentives to invest heavily in preparing proposals. Lotteries could also improve efficiency by compelling administrators to de-emphasize grant success as a primary measure of professional achievement. If lotteries are politically untenable, another remedy would be to fund researchers based on their previous research successes, although in such a way that avoids establishing barriers to entry for junior scientists or scientists from historically underrepresented demographic groups.

Science of science

Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise-for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.

Publication bias and the canonization of false facts

Science is facing a “replication crisis” in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community’s confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.

DOI:http://dx.doi.org/10.7554/eLife.21451.001

Theory, models and biology

Theoretical ideas have a rich history in many areas of biology, and new theories and mathematical models have much to offer in the future.

Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics

How can antimicrobial drugs be deployed optimally during infectious disease epidemics? Our mathematical models show it is optimal to delay treatment to maximize successful treatments. In formulating policy, however, this must be balanced against the risk of incorrectly predicting the peak of an epidemic.

Background: Although the emergence and spread of antibiotic resistance have been well studied for endemic infections, comparably little is understood for epidemic infections such as influenza. The availability of antimicrobial treatments for epidemic diseases raises the urgent question of how to deploy treatments to achieve maximum benefit despite resistance evolution. Recent simulation studies have shown that the number of cases prevented by antimicrobials can be maximized by delaying the use of treatments during an epidemic. Those studies focus on indirect effects of antimicrobial use: preventing disease among untreated individuals. Here, we identify and examine direct effects of antimicrobial use: the number of successfully treated cases.

Methodology: We develop mathematical models to study how the schedule of antiviral use influences the success or failure of subsequent use due to the spread of resistant strains.

Results: Direct effects are maximized by postponing drug use, even with unlimited stockpiles of drugs. This occurs because the early use of antimicrobials disproportionately drives emergence and spread of antibiotic resistance, leading to subsequent treatment failure. However, for antimicrobials with low effect on transmission, the relative benefit of delaying antimicrobial deployment is greatly reduced and can only be reaped if the trajectory of the epidemic can be accurately estimated early.

Conclusions and implications: Health planners face uncertainties during epidemics, including the possibility of early containment. Hence, despite the optimal deployment time near the epidemic peak, it will often be preferable to initiate widespread antimicrobial use as early as possible, particularly if the drug is ineffective in reducing transmission.

Applying evolutionary biology to address global challenges

Two categories of evolutionary challenges result from escalating human impacts on the planet. The first arises from cancers, pathogens, and pests that evolve too quickly and the second, from the inability of many valued species to adapt quickly enough. Applied evolutionary biology provides a suite of strategies to address these global challenges that threaten human health, food security, and biodiversity. This Review highlights both progress and gaps in genetic, developmental, and environmental manipulations across the life sciences that either target the rate and direction of evolution or reduce the mismatch between organisms and human-altered environments. Increased development and application of these underused tools will be vital in meeting current and future targets for sustainable development.

Honest signalling with costly gambles

Costly signalling theory is commonly invoked as an explanation for how honest communication can be stable when interests conflict. However, the signal costs predicted by costly signalling models often turn out to be unrealistically high. These models generally assume that signal cost is determinate. Here, we consider the case where signal cost is instead stochastic. We examine both discrete and continuous signalling games and show that, under reasonable assumptions, stochasticity in signal costs can decrease the average cost at equilibrium for all individuals. This effect of stochasticity for decreasing signal costs is a fundamental mechanism that probably acts in a wide variety of circumstances.

The role of gender in scholarly authorship

Gender disparities appear to be decreasing in academia according to a number of metrics, such as grant funding, hiring, acceptance at scholarly journals, and productivity, and it might be tempting to think that gender inequity will soon be a problem of the past. However, a large-scale analysis based on over eight million papers across the natural sciences, social sciences, and humanities reveals a number of understated and persistent ways in which gender inequities remain. For instance, even where raw publication counts seem to be equal between genders, close inspection reveals that, in certain fields, men predominate in the prestigious first and last author positions. Moreover, women are significantly underrepresented as authors of single-authored papers. Academics should be aware of the subtle ways that gender disparities can occur in scholarly authorship.

Bivalve network reveals latitudinal selectivity gradient at the end-Cretaceous mass extinction

Biogeographic patterns of survival help constrain the causal factors responsible for mass extinction. To test whether biogeography influenced end-Cretaceous (K-Pg) extinction patterns, we used a network approach to delimit biogeographic units (BUs) above the species level in a global Maastrichtian database of 329 bivalve genera. Geographic range is thought to buffer taxa from extinction, but the number of BUs a taxon occurred in superseded geographic range as an extinction predictor. Geographically, we found a latitudinal selectivity gradient for geographic range in the K-Pg, such that higher latitude BUs had lower extinction than expected given the geographic ranges of the genera, implying that (i) high latitude BUs were more resistant to extinction, (ii) the intensity of the K-Pg kill mechanism declined with distance from the tropics, or (iii) both. Our results highlight the importance of macroecological structure in constraining causal mechanisms of extinction and estimating extinction risk of taxa.

Between cheap and costly signals: the evolution of partially honest communication

Costly signalling theory has become a common explanation for honest communication when interests conflict. In this paper, we provide an alternative explanation for partially honest communication that does not require significant signal costs. We show that this alternative is at least as plausible as traditional costly signalling, and we suggest a number of experiments that might be used to distinguish the two theories.

Nodal dynamics, not degree distributions, determine the structural controllability of complex networks

Structural controllability has been proposed as an analytical framework for making predictions regarding the control of complex networks across myriad disciplines in the physical and life sciences (Liu et al., Nature:473(7346):167–173, 2011). Although the integration of control theory and network analysis is important, we argue that the application of the structural controllability framework to most if not all real-world networks leads to the conclusion that a single control input, applied to the power dominating set, is all that is needed for structural controllability. This result is consistent with the well-known fact that controllability and its dual observability are generic properties of systems. We argue that more important than issues of structural controllability are the questions of whether a system is almost uncontrollable, whether it is almost unobservable, and whether it possesses almost pole-zero cancellations.

Evolution and medicine in undergraduate education: a prescription for all biology students

The interface between evolutionary biology and the biomedical sciences promises to advance understanding of the origins of genetic and infectious diseases in humans, potentially leading to improved medical diagnostics, therapies, and public health practices. The biomedical sciences also provide unparalleled examples for evolutionary biologists to explore. However, gaps persist between evolution and medicine, for historical reasons and because they are often perceived as having disparate goals. Evolutionary biologists have a role in building a bridge between the disciplines by presenting evolutionary biology in the context of human health and medical practice to undergraduates, including premedical and preprofessional students. We suggest that students will find medical examples of evolution engaging. By making the connections between evolution and medicine clear at the undergraduate level, the stage is set for future health providers and biomedical scientists to work productively in this synthetic area. Here, we frame key evolutionary concepts in terms of human health, so that biomedical examples may be more easily incorporated into evolution courses or more specialized courses on evolutionary medicine. Our goal is to aid in building the scientific foundation in evolutionary biology for all students, and to encourage evolutionary biologists to join in the integration of evolution and medicine.

Evolutionary biology within medicine: a perspective of growing value

Evolutionary biology provides an essential perspective on the determinants of health and disease, believe Peter Gluckman and Carl Bergstrom. It needs to be further integrated into medical research and teaching

Evolutionary principles and their practical application

Evolutionary principles are now routinely incorporated into medicine and agriculture. Examples include the design of treatments that slow the evolution of resistance by weeds, pests, and pathogens, and the design of breeding programs that maximize crop yield or quality. Evolutionary principles are also increasingly incorporated into conservation biology, natural resource management, and environmental science. Examples include the protection of small and isolated populations from inbreeding depression, the identification of key traits involved in adaptation to climate change, the design of harvesting regimes that minimize unwanted life-history evolution, and the setting of conservation priorities based on populations, species, or communities that harbor the greatest evolutionary diversity and potential. The adoption of evolutionary principles has proceeded somewhat independently in these different fields, even though the underlying fundamental concepts are the same. We explore these fundamental concepts under four main themes: variation, selection, connectivity, and eco-evolutionary dynamics. Within each theme, we present several key evolutionary principles and illustrate their use in addressing applied problems. We hope that the resulting primer of evolutionary concepts and their practical utility helps to advance a unified multidisciplinary field of applied evolutionary biology.

The Eigenfactor MetricsTM: A Network Approach to Assessing Scholarly Journals

Limited time and budgets have created a legitimate need for quantitative measures of scholarly work. The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network. The Eigenfactor Metrics—Eigenfactor Score and Article Influence Score—use an iterative ranking scheme similar to Google’s PageRank algorithm. By this approach, citations from top journals are weighted more heavily than citations from lower-tier publications. Here we describe these metrics and the rankings that they provide.

The fitness value of information

Communication and information are central concepts in evolutionary biology. In fact, it is hard to find an area of biology where these concepts are not used. However, quantifying the information transferred in biological interactions has been difficult. How much information is transferred when the first spring rainfall hits a dormant seed, or when a chick begs for food from its parent? One measure that is commonly used in such cases is fitness value: by how much, on average, an individual's fitness would increase if it behaved optimally with the new information, compared to its average fitness without the information. Another measure, often used to describe neural responses to sensory stimuli, is the mutual information-a measure of reduction in uncertainty, as introduced by Shannon in communication theory. However, mutual information has generally not been considered to be an appropriate measure for describing developmental or behavioral responses at the organismal level, because it is blind to function; it does not distinguish between relevant and irrelevant information. In this paper we show that there is in fact a surprisingly tight connection between these two measures in the important context of evolution in an uncertain environment. In this case, a useful measure of fitness benefit is the increase in the long-term growth rate, or the fold increase in number of surviving lineages. We show that in many cases the fitness value of a developmental cue, when measured this way, is exactly equal to the reduction in uncertainty about the environment, as described by the mutual information.

Mapping change in large networks

Change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: Interactions within and between organisms change; transportation patterns by air, land, and sea all change; the global financial flow changes; and the frontiers of scientific research change. Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. Here we show that bootstrap resampling accompanied by significance clustering provides a solution to this problem. To connect changing structures with the changing function of networks, we highlight and summarize the significant structural changes with alluvial diagrams and realize de Solla Price's vision of mapping change in science: studying the citation pattern between about 7000 scientific journals over the past decade, we find that neuroscience has transformed from an interdisciplinary specialty to a mature and stand-alone discipline.

Evolution in health and medicine Sackler colloquium: Making evolutionary biology a basic science for medicine

New applications of evolutionary biology in medicine are being discovered at an accelerating rate, but few physicians have sufficient educational background to use them fully. This article summarizes suggestions from several groups that have considered how evolutionary biology can be useful in medicine, what physicians should learn about it, and when and how they should learn it. Our general conclusion is that evolutionary biology is a crucial basic science for medicine. In addition to looking at established evolutionary methods and topics, such as population genetics and pathogen evolution, we highlight questions about why natural selection leaves bodies vulnerable to disease. Knowledge about evolution provides physicians with an integrative framework that links otherwise disparate bits of knowledge. It replaces the prevalent view of bodies as machines with a biological view of bodies shaped by evolutionary processes. Like other basic sciences, evolutionary biology needs to be taught both before and during medical school. Most introductory biology courses are insufficient to establish competency in evolutionary biology. Premedical students need evolution courses, possibly ones that emphasize medically relevant aspects. In medical school, evolutionary biology should be taught as one of the basic medical sciences. This will require a course that reviews basic principles and specific medical applications, followed by an integrated presentation of evolutionary aspects that apply to each disease and organ system. Evolutionary biology is not just another topic vying for inclusion in the curriculum; it is an essential foundation for a biological understanding of health and disease.

Little evidence for genetic susceptibility to influenza A (H5N1) from family clustering data

The apparent clustering of human cases of influenza A (H5N1) among blood relatives has been considered as evidence of genetic variation in susceptibility. We show that, by chance alone, a high proportion of clusters are expected to be limited to blood relatives when infection is a rare event.

Maps of random walks on complex networks reveal community structure

To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

An information-theoretic framework for resolving community structure in complex networks

To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.

An information-theoretic framework for resolving community structure in complex networks

To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.

The evolution of functionally referential meaning in a structured world

Animal communication systems serve to transfer both motivational information--about the intentions or emotional state of the signaler--and referential information--about external objects. Although most animal calls seem to deal primarily with motivational information, those with a substantial referential component are particularly interesting because they invite comparison with words in human language. We present a game-theoretic model of the evolution of communication in a "structured world", where some situations may be more similar to one another than others, and therefore require similar responses. We find that breaking the symmetry in this way creates the possibility for a diverse array of evolutionarily stable communication systems. When the number of signals is limited, as in alarm calling, the system tends to evolve to group together situations which require similar responses. We use this observation to make some predictions about the situations in which primarily motivational or referential communication systems will evolve.

On the evolutionary origin of aging

It is generally believed that the first organisms did not age, and that aging thus evolved at some point in the history of life. When and why this transition occurred is a fundamental question in evolutionary biology. Recent reports of aging in bacteria suggest that aging predates the emergence of eukaryotes and originated in simple unicellular organisms. Here we use simple models to study why such organisms would evolve aging. These models show that the differentiation between an aging parent and a rejuvenated offspring readily evolves as a strategy to cope with damage that accumulates due to vital activities. We use measurements of the age-specific performance of individual bacteria to test the assumptions of the model, and find evidence that they are fulfilled. The mechanism that leads to aging is expected to operate in a wide range of organisms, suggesting that aging evolved early and repeatedly in the history of life. Aging might thus be a more fundamental aspect of cellular organisms than assumed so far.

Pandemic influenza: risk of multiple introductions and the need to prepare for them

Containing an emerging influenza H5N1 pandemic in its earliest stages may be feasible, but containing multiple introductions of a pandemic-capable strain would be more difficult. Mills and colleagues argue that multiple introductions are likely, especially if risk of a pandemic is high.

How do adaptive immune systems control pathogens while avoiding autoimmunity?

Immune systems face a daunting control challenge. On the one hand, they need to minimize damage from pathogens, without wasting energy and resources, but on the other must avoid initiating or perpetuating autoimmune responses. Finally, because pathogens interfere with immune function, immune systems must be robust against sabotage. We describe here how these challenges are met by two immune systems, the intracellular RNA interference system and the vertebrate CD8 T-cell response. We extrapolate from these two systems to propose principles for strategically robust control.

A population-epigenetic model to infer site-specific methylation rates from double-stranded DNA methylation patterns

Cytosine methylation is an epigenetic mechanism in eukaryotes that is often associated with stable transcriptional silencing, such as in X-chromosome inactivation and genomic imprinting. Aberrant methylation patterns occur in several inherited human diseases and in many cancers. To understand how methylated and unmethylated states of cytosine residues are transmitted during DNA replication, we develop a population-epigenetic model of DNA methylation dynamics. The model is informed by our observation that de novo methylation can occur on the daughter strand while leaving the opposing cytosine unmethylated, as revealed by the patterns of methylation on the two complementary strands of individual DNA molecules. Under our model, we can infer site-specific rates of both maintenance and de novo methylation, values that determine the fidelity of methylation inheritance, from double-stranded methylation data. This approach can be used for populations of cells obtained from individuals without the need for cell culture. We use our method to infer cytosine methylation rates at several sites within the promoter of the human gene FMR1.