A model-driven discovery process, Computing Life, is used to identify an ensemble of genetic networks that describe the biological clock. A clock mechanism involving the genes white-collar-1 and white-collar-2 (wc-1 and wc-2) that encode a transcriptional activator (as well as a blue-light receptor) and an oscillator frequency (frq) that encodes a cyclin that deactivates the activator is used to guide this discovery process through three cycles of microarray experiments. Central to this discovery process is a new methodology for the rational design of a Maximally Informative Next Experiment (MINE), based on the genetic network ensemble. In each experimentation cycle, the MINE approach is used to select the most informative new experiment in order to mine for clock-controlled genes, the outputs of the clock. As much as 25% of the N. crassa transcriptome appears to be under clock-control. Clock outputs include genes with products in DNA metabolism, ribosome biogenesis in RNA metabolism, cell cycle, protein metabolism, transport, carbon metabolism, isoprenoid (including carotenoid) biosynthesis, development, and varied signaling processes. Genes under the transcription factor complex WCC ( = WC-1/WC-2) control were resolved into four classes, circadian only (612 genes), light-responsive only (396), both circadian and light-responsive (328), and neither circadian nor light-responsive (987). In each of three cycles of microarray experiments data support that wc-1 and wc-2 are auto-regulated by WCC. Among 11,000 N. crassa genes a total of 295 genes, including a large fraction of phosphatases/kinases, appear to be under the immediate control of the FRQ oscillator as validated by 4 independent microarray experiments. Ribosomal RNA processing and assembly rather than its transcription appears to be under clock control, suggesting a new mechanism for the post-transcriptional control of clock-controlled genes.

A chemical reaction network for the regulation of the quinic acid (qa) gene cluster of Neurospora crassa is proposed. An efficient Monte Carlo method for walking through the parameter space of possible chemical reaction networks is developed to identify an ensemble of deterministic kinetics models with rate constants consistent with RNA and protein profiling data. This method was successful in identifying a model ensemble fitting available RNA profiling data on the qa gene cluster. With genome sequencing projects supplying an almost complete inventory of the building blocks of life, functional genomics is now facing the challenge of ''re-assembling the pieces'' (1, 2). Time-dependent mRNA (3) and protein profiling (4), protein-protein (5-8) and protein-DNA (9) interaction mapping, and the in vitro reconstruction of reaction networks (10, 11) are providing insight into the topology and kinetics of a living cell's full biochemical and gene regulatory circuitry. For the first time, it is now possible to place a particular biological circuit like those describing carbon metabolism, transcription, cell cycle, or the biological clock in simple eukaryotes in a larger context, and to examine the coupling of these circuits (12).New tools in computational biology are needed to identify these reaction networks by using well studied subcircuits like those for carbon metabolism, cell cycle, or the biological clock as a launch point into the entire circuit of a living cell. The qa gene cluster of Neurospora crassa and the GAL gene cluster of Saccharomyces cerevisiae in carbon metabolism have served as early paradigms for eukaryotic gene regulation (13,14) and are prime candidates for taking a genomic perspective on biological circuits. Mechanisms of regulation in the qa and GAL clusters with their transcriptional activator and repressors are also shared with many other regulatory networks. Because of their relative simplicity, they also provide an opportunity to test new genomic approaches to identifying chemical reaction networks or biological circuits that underlie many fundamental biological processes (15). Three opportunities exist now for identifying and refining biological circuits: the accumulation of transcriptional profiling data (3), a growing number of approaches to modeling gene regulation (11,(15)(16)(17)(18)(19)(20)(21), and the ability to carry out the in vitro reconstruction of biological circuits with a diversity of emergent properties including bistable (10) and oscillatory activity (11).However, initially, the profiling data will be scarce and the unknown parameters plentiful. Identification of the parameters in a reaction network is further complicated by the facts that the data are noisy and that our knowledge of the underlying reaction network's topology and of its participating molecular species is incomplete, even in well studied networks like those for the -switch, lac operon, trp operon, or GAL cluster. To circumvent these difficulties, we present a statistical modeling approach called the ensemble m...

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell-cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with datadriven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.agent-based simulation | image processing | emergent behavior | fluorescent imaging | cell communication C ollective cell migration is essential for many developmental processes, including fruiting body development of myxobacteria (1) and Dictyostelium (2), embryonic gastrulation (3, 4), and neural crest development (5). Conversely, cancer cell metastases represent detrimental migratory events that disseminate dysfunctional cells (6). In all these processes, a population of cells leaves its current location and migrates in a coordinated manner to new locations where motility becomes reduced. Remarkable progress has been made in studying the intracellular machinery of these organisms (7). Much less is known about the systemlevel coordination of cell migration. Cell movement in these systems is a 3D, dynamic process coordinated by a combination of diverse physical and chemical cues acting on the cells (3,5,8). Recent developments in tracking individual cell movement in vivo have provided unprecedented detail and revealed surprising levels of heterogeneity (5, 7). Reverse engineering of how these individual cell movements lead to collective migration patterns has proved difficult. Whereas computational models are able to test whether a given set of ad hoc assumptions lead to e...

We address the question of whether anisotropic superconductivity is compatible with the evidently weak sensitivity of the critical temperature T c to sample quality in the high-T c copper oxides. We examine this issue quantitatively by solving the strongcoupling Eliashberg equations numerically as well as analytically for s-wave impurity scattering within the second Born approximation. For pairing interactions with a characteristically low energy scale, we find an approximately universal dependence of the d-wave superconducting transition temperature on the planar residual resistivity which is independent of the details of the microscopic pairing. These results, in conjunction with future systematic experiments, should help elucidate the symmetry of the order parameter in the cuprates.

A diverse array of organisms from bacteria to humans may have evolved the ability to tell time in the presence or absence of external environmental cues. In the lowly bread mould, Neurospora crassa, biomolecular reactions involving the white-collar-1 (wc-1), white-collar-2 (wc-2), and frequency (frq) genes and their products constitute building blocks of a biological clock. Here we use genetic network models to explain quantitatively, from a systems perspective, how these building blocks interact, and how a complex trait like clock oscillation emerges from these interactions. We use a recently developed method of genetic network identification to find an ensemble of oscillating network models quantitatively consistent with available RNA and protein profiling data on the N. crassa clock. Predicted key features of the N. crassa clock system are a dynamically frustrated closed feedback loop, cooperativity in frq gene activation, and/or WC-1/WC-2 protein complex deactivation and substantial posttranscriptional enhancement of wc-1 RNA lifetime. Measuring the wc-1 mRNA lifetime provides a critical test of the genetic networks. (1) and are at the heart of a new systems approach to biology (2). The biological clock (3) provides a prototypical and biologically ubiquitous example of how a complex trait can emerge from the interaction of even a small number of gene regulatory elements. In an experimentally well studied example, the filamentous fungus, Neurospora crassa, and the biomolecular reactions involving the white-collar-1, white-collar-2, and frequency genes and their products constitute building blocks of a biological clock (4). A central open question of systems biology is whether these building blocks are necessary and sufficient to define a circuit or genetic network that oscillates, and how, in quantitative detail, such oscillations emerge from the interactions among these building blocks. Here we use a recently developed method of genetic network identification (5) to find an ensemble of oscillating network models, constituted from wc-1, wc-2, and frq and their products, which is quantitatively consistent with available RNA and protein profiling data on the N. crassa biological clock. The use of genetic networks to integrate diverse experimental information and to predict the behavior of a complex trait, such as the biological clock, provides a new paradigm for quantitative genetics at the molecular level (6).Key features of the genetic network that permit oscillations are: (i) the presence of functional wc-1, wc-2, and frq genes, generating protein products WC-1, WC-2, and FRQ, and the white collar complex (WCC) formed by WC-1 and WC-2; (ii) a closed-feedback loop of the biomolecular reactions in the genetic network, with WCC activating the frq gene 3 the activated frq gene producing frq mRNA 3 frq mRNA producing FRQ protein and 3 FRQ protein deactivating WCC; (iii) dynamical frustration arising in the feedback loop because of WCC's stimulation of the production of FRQ, whereas FRQ induces the deactivation o...

The synchronization of stochastic coupled oscillators is a central problem in physics and an emerging problem in biology, particularly in the context of circadian rhythms. Most measurements on the biological clock are made at the macroscopic level of millions of cells. Here measurements are made on the oscillators in single cells of the model fungal system, Neurospora crassa, with droplet microfluidics and the use of a fluorescent recorder hooked up to a promoter on a clock controlled gene-2 (ccg-2). The oscillators of individual cells are stochastic with a period near 21 hours (h), and using a stochastic clock network ensemble fitted by Markov Chain Monte Carlo implemented on general-purpose graphical processing units (or GPGPUs) we estimated that >94% of the variation in ccg-2 expression was stochastic (as opposed to experimental error). To overcome this stochasticity at the macroscopic level, cells must synchronize their oscillators. Using a classic measure of similarity in cell trajectories within droplets, the intraclass correlation (ICC), the synchronization surface ICC is measured on >25,000 cells as a function of the number of neighboring cells within a droplet and of time. The synchronization surface provides evidence that cells communicate, and synchronization varies with genotype.

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