Hierarchy and Feedback in the Evolution of the E. coli Transcription Network

The E.coli transcription network has an essentially feedforward structure, with, however, abundant feedback at the level of self-regulations. Here, we investigate how these properties emerged during evolution. An assessment of the role of gene duplication based on protein domain architecture shows that (i) transcriptional autoregulators have mostly arisen through duplication, while (ii) the expected feedback loops stemming from their initial cross-regulation are strongly selected against. This requires a divergent coevolution of the transcription factor DNA-binding sites and their respective DNA cis-regulatory regions. Moreover, we find that the network tends to grow by expansion of the existing hierarchical layers of computation, rather than by addition of new layers. We also argue that rewiring of regulatory links due to mutation/selection of novel transcription factor/DNA binding interactions appears not to significantly affect the network global hierarchy, and that horizontally transferred genes are mainly added at the bottom, as new target nodes. These findings highlight the important evolutionary roles of both duplication and selective deletion of crosstalks between autoregulators in the emergence of the hierarchical transcription network of E.coli.Comment: to appear in PNA

Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks

Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.Comment: 9 pages, 6 figure

Rethinking the Dutch Innovation Agenda: Management and Organization Matter Most

In this essay, we challenge the present dominant emphasis in the Dutch Innovation Debate on the creation of technological innovations, the focus on a few core technologies, and the allocation of more financial resources. We argue that managerial capabilities and organizing principles for innovation should have a higher priority on the Dutch Innovation Agenda. Managerial capabilities for innovation deal with cognitive elements such as the capacity to absorb knowledge, create entrepreneurial mindsets, and facilitate managerial experimentation and higher-order learning abilities. These capacities can only be developed by distinctive managerial roles that enhance hierarchy, teaming and shared norms. Utilizing these unique managerial capabilities requires novel organizing principles, such as managing internal rates of change, nurturing self-organization and balancing high levels of exploration and exploitation. These managerial capabilities and organizing principles of innovation create new sources of productivity growth and competitive advantage.The dramatic fall back of the Netherlands in the league of innovative and high productivity countries of the World Economic Forum-Report can be mainly attributed to the present lack in the Netherlands of these key managerial and organizational enablers of innovation and productivity growth. We provide various levers for building unique managerial capabilities and novel organizing principles of innovation. Moreover, we describe the necessary roles that different actors have to play in this innovation arena. In particular, we focus on the often neglected but important role of strategic regulations that speed up innovation and productivity growth. They are the least expensive way to boost innovation in organizations in both the Dutch private and public sector. Finally, we discuss the implications for the Dutch Innovation Agenda. It should start with setting a challenging ambition, namely the return of The Netherlands within the WEF- league of the top-ten most innovative and productive countries of the world. Considering the under-utilization of available knowledge stemming from technological innovations, managerial and organizational determinants of innovation should receive first priority. These determinants have a high strategic relevance and should receive more public recognition. We suggest to organize an annual innovation ranking of the most outstanding Dutch firms, to develop an innovation audit that measures firms’ non-technological innovation capacity, and to create an overall innovation policy for fast diffusion of new managerial capabilities and adequate organizing principles throughout the Dutch private and public sector.In conclusion, we add five new items to the Dutch Innovation Agenda:1. Prioritize administrative innovationsInvestments in management and organization determinants of absorption of knowledge and its successful application (administrative innovation) should have a higher priority than investments in technological innovations.2. Build new managerial capabilities and develop novel organizing principlesFor these administrative innovations to succeed, firms have to build managerial capabilities (broad knowledge-base, absorptive capacity, managerial experimentation, higher-order learning) and various management roles (hierarchy, teaming, shared norms) to increase the assimilation of external knowledge and the utilization for innovation. Moreover, they have to develop novel organizing principles that increase internal rates of change, nurture self-organization and synchronize high levels of exploration and exploitation.3. Set levers of innovation by creating selection environments that favor innovation and by redefining the roles of key actors Management has to create a proper organizational context to foster entrepreneurship and innovation (internal selection environment). Governmental agencies have to focus on innovation and productivity enabling strategic regulations (external selection environment). Moreover, research institutes, business schools, and consulting firms should not only focus on technological knowledge, but also on managerial and organizational knowledge for innovation. In the end, private small and large firms and public institutions have to recognize that they all must contribute to the national goal of increasing innovation and productivity growth.4. Create a new challenging national ambition: return of the Netherlands within the top-10The Netherlands has to return to the top-ten most innovative and productive countries in the world as reflected in international rankings such as the World Economic Forum’s Global Competitiveness Index.5. Proliferate an awareness and passion for innovation:Create public awareness and recognition of the societal relevance of outstanding managerial capabilities and organizing principles to innovation and productivity growth:o Initiate a Dutch innovation ranking in terms of management and organization;o Develop proper assessment tools for innovations in management and organization;o Enhance reporting on the progress on managerial and organizational innovation as part of modern corporate governance and as part of outstanding annual reports.These issues may contribute to rethinking the fundamental sources of innovation, productivity growth and sustainable competitive advantage of the Dutch economy.dynamic capabilities;knowledge transfer;exploitation;exploration;mANAGEMENT;mindsets;organizing pinciples;srategic rgulation;strategy innovation

A COEVOLUTIONARY APPROACH TO UNDERSTANDING THE PARADOX OF SOCIAL PRESSURES VERSUS ECONOMIC EFFICIENCY ACROSS THE WORLD'S FOOD CHAINS

Institutional and Behavioral Economics,

Evolution of sparsity and modularity in a model of protein allostery

The sequence of a protein is not only constrained by its physical and biochemical properties under current selection, but also by features of its past evolutionary history. Understanding the extent and the form that these evolutionary constraints may take is important to interpret the information in protein sequences. To study this problem, we introduce a simple but physical model of protein evolution where selection targets allostery, the functional coupling of distal sites on protein surfaces. This model shows how the geometrical organization of couplings between amino acids within a protein structure can depend crucially on its evolutionary history. In particular, two scenarios are found to generate a spatial concentration of functional constraints: high mutation rates and fluctuating selective pressures. This second scenario offers a plausible explanation for the high tolerance of natural proteins to mutations and for the spatial organization of their least tolerant amino acids, as revealed by sequence analyses and mutagenesis experiments. It also implies a faculty to adapt to new selective pressures that is consistent with observations. Besides, the model illustrates how several independent functional modules may emerge within a same protein structure, depending on the nature of past environmental fluctuations. Our model thus relates the evolutionary history and evolutionary potential of proteins to the geometry of their functional constraints, with implications for decoding and engineering protein sequences

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system