In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

Integrating research experiences into undergraduate life sciences curricula in the form of course-based undergraduate research experiences (CUREs) can meet national calls for education reform by giving students the chance to “do science.” In this article, we provide a step-by-step practical guide to help instructors assess their CUREs using best practices in assessment. We recommend that instructors first identify their anticipated CURE learning outcomes, then work to identify an assessment instrument that aligns to those learning outcomes and critically evaluate the results from their course assessment. To aid instructors in becoming aware of what instruments have been developed, we have also synthesized a table of “off-the-shelf” assessment instruments that instructors could use to assess their own CUREs. However, we acknowledge that each CURE is unique and instructors may expect specific learning outcomes that cannot be assessed using existing assessment instruments, so we recommend that instructors consider developing their own assessments that are tightly aligned to the context of their CURE.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Although gender gaps have been a major concern in male-dominated science, technology, engineering, and mathematics disciplines such as physics and engineering, the numerical dominance of female students in biology has supported the assumption that gender disparities do not exist at the undergraduate level in life sciences. Using data from 23 large introductory biology classes for majors, we examine two measures of gender disparity in biology: academic achievement and participation in whole-class discussions. We found that females consistently underperform on exams compared with males with similar overall college grade point averages. In addition, although females on average represent 60% of the students in these courses, their voices make up less than 40% of those heard responding to instructor-posed questions to the class, one of the most common ways of engaging students in large lectures. Based on these data, we propose that, despite numerical dominance of females, gender disparities remain an issue in introductory biology classrooms. For student retention and achievement in biology to be truly merit based, we need to develop strategies to equalize the opportunities for students of different genders to practice the skills they need to excel.

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

Bioethics is an important aspect of the core competency of biology of understanding the relationship between science and society, but because of the controversial nature of the topics covered in bioethics courses, different groups of students may experience identity conflicts or discomfort when learning about them. However, no previous studies have investigated the impact of undergraduate bioethics students’ experiences in bioethics courses on their opinions and comfort. To fill this gap in knowledge, we investigated undergraduate bioethics students’ attitudes about and comfort when learning abortion, gene editing, and physician assisted suicide, as well as how their gender, religious, and political identity influence their attitudes and changes in their attitudes after instruction. We found that religious students were less supportive of gene editing, abortion, and physician assisted suicide than nonreligious students, non-liberal students were less supportive of abortion and physician assisted suicide than liberal students, and women were less supportive of abortion than men. Additionally, we found that religious students were less comfortable than nonreligious students when learning about gene editing, abortion, and physician assisted suicide, and non-liberal students were less comfortable than liberal students when learning about abortion. When asked how their comfort could have been improved, those who felt that their peers or instructors could have done something to increase their comfort most commonly cited that including additional unbiased materials or incorporating materials and discussions that cover both sides of every controversial issue would have helped them to feel more comfortable when learning about gene editing, abortion, and physician assisted suicide. Finally, we found that students who were less comfortable learning about abortion and physician assisted suicide were less likely to participate in discussions regarding those topics. Our findings show that students in different groups not only tend to have different support for controversial topics like gene editing, abortion, and physician assisted suicide, but they also feel differentially comfortable when learning about them, which in turn impacts their participation. We hope that this work helps instructors to recognize the importance of their students’ comfort to their learning in bioethics courses, and from this study, they can take away the knowledge that students feel their comfort could be most improved by the incorporation of additional inclusive materials and course discussions regarding the controversial topics covered in the course.