Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of approximately 15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an "extracellular interaction" division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.

We describe a system in which autonomous robots assemble two-dimensional structures out of square building blocks. A fixed set of local control rules is sufficient for a group of robots to collectively build arbitrary solid structures. We present and compare four versions in which blocks are (1) inert and indistinguishable, (2) uniquely labeled, (3) able to be relabeled by robots, (4) capable of some computation and local communication. Added block capabilities increase the availability of nonlocal structural knowledge, thereby increasing robustness and significantly speeding construction. In this way we extend the principle of stigmergy (storing information in the environment) used by social insects, by increasing the capabilities of the blocks that represent that environmental information. Finally, we describe hardware experiments using a prototype capable of building arbitrary solid 2-D structures

Distributed computing systems are increasingly being created as self-organizing collections of many autonomous (human or software) agents cooperating as peers. Peer-to-peer coordination introduces, however, unique and potentially serious challenges. When there is no one "in charge", dysfunctions can emerge as the collective effect of locally reasonable decisions. In this paper, we consider the dysfunction wherein inefficient resource use oscillations occur due to delayed status information, and describe novel approaches, based on the selective use of misinformation, for dealing with this problem. A model of several servers offering equivalent service to independent clients is presented and studied numerically and analytically; the spreading of misinformation about the queue status is found to dampen oscillations and improve system performance for a wide range of parameters.

We review the lessons learned from problems with systems engineering over the past couple of decades and suggest that there are two effective strategies for overcoming them: (1) restricting the conventional systems engineering process to not-too-complex projects, and (2) adopting an evolutionary paradigm for complex systems engineering that involves rapid parallel exploration and a context designed to promote change through competition between design/implementation groups with field testing of multiple variants. The second approach is an extension of many of the increasingly popular variants of systems engineering today.

Collaborative design is challenging because strong interdependencies between design issues make it difficult to converge on a single design that satisfies these dependencies and is acceptable to all participants. Complex systems research has much to offer to the understanding of these dynamics. This paper describes some insights from the complex systems perspective.