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All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide.Robustness and Evolvability in Living Systemstackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness. Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness. Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.

Real networks often grow through the sequential addition of new nodes that connect to older ones in the graph. However, many real systems evolve through the branching of fundamental units, whether those be scientific fields, countries, or species. Here, we provide empirical evidence for self-similar growth of network structure in the evolution of real systems—the journal-citation network and the world trade web—and present the geometric branching growth model, which predicts this evolution and explains the symmetries observed. The model produces multiscale unfolding of a network in a sequence of scaled-up replicas preserving network features, including clustering and community structure, at all scales. Practical applications in real instances include the tuning of network size for best response to external influence and finite-size scaling to assess critical behavior under random link failures.

Embeddedness in social networks is increasingly seen as a root cause of human achievement, social stratification, and actor behavior. In this article, we review sociological research that examines the processes through which dyadic ties form, persist, and dissolve. Three sociological mechanisms are overviewed: assortative mechanisms that draw attention to the role of actors' attributes, relational mechanisms that emphasize the influence of existing relationships and network positions, and proximity mechanisms that focus on the social organization of interaction.

Angiosperms have a complex history of whole-genome duplications (WGDs), with varying numbers and ages of WGD events across clades. These WGDs have greatly affected the composition of plant genomes due to the biased retention of genes belonging to certain functional categories following their duplication. In particular, regulatory genes and genes encoding proteins that act in multiprotein complexes have been retained in excess following WGD. Here, we inferred protein–protein interaction (PPI) networks and gene regulatory networks (GRNs) for seven well-characterized angiosperm species and explored the impact of both WGD and small-scale duplications (SSDs) in network topology by analyzing changes in frequency of network motifs. We found that PPI networks are enriched in WGD-derived genes associated with dosage-sensitive intricate systems, and strong selection pressures constrain the divergence of WGD-derived genes at the sequence and PPI levels. WGD-derived genes in network motifs are mostly associated with dosage-sensitive processes, such as regulation of transcription and cell cycle, translation, photosynthesis, and carbon metabolism, whereas SSD-derived genes in motifs are associated with response to biotic and abiotic stress. Recent polyploids have higher motif frequencies than ancient polyploids, whereas WGD-derived network motifs tend to be disrupted on the longer term. Our findings demonstrate that both WGD and SSD have contributed to the evolution of angiosperm GRNs, but in different ways, with WGD events likely having a more significant impact on the short-term evolution of polyploids.

Abstract As species diverge, a wide range of evolutionary processes lead to changes in protein–protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug–drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

This study explores the evolutionary characteristics and driving factors of innovative cooperation networks in the field of carbon capture, utilization, and storage (CCUS) technology in China, laying a foundation for the governance of those networks. Taking the patents in the field of CCUS technology in China as the research object, this study analyzes the evolutionary characteristics of innovative cooperation networks based on social network theory. The exponential random graph model (ERGM) reveals the factors driving the evolution of innovative cooperation networks from three perspectives: endogenous structure, node assortment, and node attribute. Based on the technology life cycle theory and the network topology characteristics of each stage, this study reveals a four-stage evolution of the China CCUS innovation network, including the fragmented exploration network (1988–2007), the star-shaped radiation network (2008–2011), the multi-core structured network (2012–2018), and the cross-domain synergistic integration network. ERGM analysis indicates that star-shaped structures and closed triads are the core endogenous driving forces promoting the evolution of the innovation collaboration network in the CCUS technology field. The geographical adjacency effect weakens as the stages progress. The promoting effect of organizational assortment on network evolution and development begins to emerge. In contrast, the role of R&D capability shifts from facilitation to inhibition, and the “Matthew Effect” becomes ineffective. Meanwhile, the structural hole inhibition effect reveals the predicament of technological barriers. Constructing an efficient and interactive innovation collaboration network for CCUS in China requires adherence to the rigid coupling requirements inherent in the CCUS technology chain. It is essential to enhance substantive collaboration based on geographical adjacency while addressing collaboration barriers arising from structural hole effects and technological monopolies.

This multimethod study investigates the effects of entrepreneurs' interpersonal networking style on the initiation of interorganizational exchange ties. I use inductive theorizing to make a distinction between interpersonal networking actions aimed at adding new contacts (network-broadening actions) versus managing existing contacts (network-deepening actions). I reason that because networking actions alter the cost-benefit calculus of using referrals, the extent to which entrepreneurs rely on referrals when searching for new exchange partners should vary with their networking actions. I then propose that entrepreneurs are likely to add fewer new exchange partners when they rely more on referrals to search. The empirical analysis employs a longitudinal design using data coded from the business cards of new contacts formed over a two-month period by a panel of Indian entrepreneurs operating business-to-business ventures. This study makes a theoretical contribution by identifying decision makers' networking style as a distinct mechanism shaping partner selection for their organization. Specifically, the study shows entrepreneurs using more network-deepening actions initiate fewer new economic exchanges, due (in part) to their increased reliance on referral-based search, whereas entrepreneurs using more network-broadening actions initiate more new economic exchanges due (in part) to their decreased reliance on referral-based search.

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli . Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node’s initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the “propinquity model,” in which we control and vary the distance of the second link established by a new node and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.

Purpose The purpose of this paper is to provide an overview of the available insights regarding interorganizational network evolution. The research questions being addressed are as follows: What is the nature of interorganizational network evolution? And what causes interorganizational network evolution? The review hence focuses on the nature of interorganizational network evolution (at the ego-network level and whole-network level) and the causes of interorganizational network evolution (firm-related causes and environmental causes). This paper highlights relevant gaps in the existing literature on interorganizational network evolution while outlining a research agenda by identifying key research questions and issues requiring further scholarly contributions to stimulate research in this field. Design/methodology/approach An extensive review of scholarly peer-reviewed English language journal articles was conducted in the subject areas of economics, sociology, business and management (including entrepreneurship) while excluding articles in the domain areas of computer science that dealt with computer networks and the health field that addressed neural networks to obtain articles on interorganizational network evolution for the period 1970-2019. Various journal databases such as EBSCO, ScienceDirect (Elsevier), Emerald, JSTOR and ABI/INFORM and Ebook Central on ProQuest were used to extract relevant articles using specific keywords. Findings To better understand this phenomenon of interorganizational network evolution, there is a need for future studies to focus on the less researched areas such as the “nature of evolution” of EINR1, EINR3 and EINR4 and the “causes of evolution” of FRC3, FRC5, FRC7 and FRC8. Further, over the years, in comparison to the evolution of interorganizational network relationships (EINR), fewer works have considered the evolution of overall interorganizational network structure (EINS). The research studies on environmental causes (EC) have been less in number in comparison to firm related causes (FRC), and this could be an area for further research. Also, studies on interorganizational network evolution have not examined the impact of FRC1 on EINR 3 and only a few studies have examined the impact of FRC1 on EINR1 and EINR4. Less attention has been given to the impact of FRC2 on EINR1, EINR3, EINR4 and EINS. Additionally, the impact of FRC3 on EINR1, EINR3 and EINS needs more in-depth examination. The impact of FRC4 on EINR4; FRC5 on EINR1, EINR2 and EINR4; FRC6 on EINR1 and EINS; and FRC7 and FRC8 on all forms of “nature of interorganizational network evolution” requires more research work. Finally, the impact of EC on EINR3 and EINR4 is also a less researched stream in the literature needing more scholarly contribution to better understand the phenomenon under consideration in this study. Some of the least explored theoretical lenses and relevant questions that can be addressed using these lenses to advance research on network evolution have also been discussed. Originality/value The main contribution of this paper is that it provides a comprehensive literature review, collating the dispersed knowledge on interorganizational network evolution – nature of evolution and causes of evolution, identifying areas that require further research attention for the development of this domain.