The Evolution of Cell Communication: The Road not Taken.

In the post-genomic era the complex problem of evolutionary biology can be tackled from the top-down, the bottom-up, or from the middle-out. Given the emergent and contingent nature of this process, we have chosen to take the latter approach, both as a mechanistic link to developmental biology and as a rational means of identifying signaling mechanisms based on their functional genomic significance. Using this approach, we have been able to configure a working model for lung evolution by reverse-engineering lung surfactant from the mammalian lung to the swim bladder of fish. Based on this archetypal cell-molecular model, we have reduced evolutionary biology to cell communication, starting with unicellular organisms communicating with the environment, followed by cell-cell communication to generate metazoa, culminating in the communication of genetic information between generations, i.e. reproduction. This model predicts the evolution of physiologic systems-including development, homeostasis, disease, regeneration/repair, and aging- as a logical consequence of biology reducing entropy. This approach provides a novel and robust way of formulating refutable, testable hypotheses to determine the ultimate origins and first principles of physiology, providing candidate genes for phenotypes hypothesized to have mediated evolutionary changes in structure and/or function. Ultimately, it will form the basis for predictive medicine and molecular bioethics, rather than merely showing associations between genes and pathology, which is an unequivocal Just So Story. In this new age of genomics, our reach must exceed our grasp

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

This paper is concerned with the modeling and evolving of Cell Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first provided, in which we describe the potential applications of modeling and evolving these biochemical networks in silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the ESIGNET project. Results obtained with these methods are summarized and discussed

Extending an eco-evolutionary understanding of biofilm-formation at the air-liquid interface to community biofilms

Growing bacterial populations diversify to produce a number of competing lineages. In the Pseudomonas fluorescens SBW25 model system, Wrinkly Spreader mutant lineages, capable of colonising the air-liquid interface of static microcosms by biofilm-formation, rapidly appear in diversifying populations with a fitness advantage over the ancestral wild-type strain. Similarly, a biofilm is rapidly produced by a community containing many biofilm-competent members, and selection by serial transfer of biofilm samples across microcosms results in a gradually changing community structure. Both the adaptive radiation producing Wrinkly Spreaders and the succession of biofilm communities in these static microcosms can be understood through evolutionary ecology in which ecological interactions and evolutionary processes are combined. Such eco-evolutionary dynamics are especially important for bacteria, as rapid growth, high population densities and strong selection in the context of infections can lead to fast changes in disease progression and resistance phenotypes, while similar changes in community function may also affect many microbially-mediated biotechnological and industrial processes. Evolutionary ecology provides an understanding of why bacterial biofilms are so prevalent and why they are such a successful colonisation strategy, and it can be directly linked to molecular analyses to understand the importance of pathways and responses involved in biofilm-formation

An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems

The authors would like to thank the support on this research by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD