Hfsp Journal最新文献

We consider a simple information-theoretic model for evolutionary dynamics approaching the "coding threshold," where the capacity to symbolically represent nucleic acid sequences emerges in response to a change in environmental conditions. We study the conditions when a coupling between the dynamics of a "proto-cell" and its proto-symbolic representation becomes beneficial in terms of preserving the proto-cell's information in a noisy environment. In particular, we are interested in understanding the behavior at the "error threshold" level, which, in our case, turns out to be a whole "error interval." The useful coupling is accompanied by self-organization of internal processing, i.e., an increase in complexity within the evolving system. Second, we study whether and how different proto-cells can stigmergically share such information via a joint encoding, even if they have slightly different individual dynamics. Implications for the emergence of biological genetic code are discussed.

Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

The circadian clock is a timekeeping mechanism that enables anticipation of daily environmental changes. In the plant Arabidopsis thaliana, the circadian system is a multiloop series of interlocked transcription-translation feedbacks. Several genes have been arranged in these oscillation loops, but the position of the core-clock gene ELF4 in this network was previously undetermined. ELF4 lacks sequence similarity to known domains, and functional homologs have not yet been identified. Here we show that ELF4 is functionally conserved within a subclade of related sequences, and forms an alpha-helical homodimer with a likely electrostatic interface that could be structurally modeled. We support this hypothesis by expression analysis of new elf4 hypomorphic alleles. These weak mutants were found to have expression level phenotypes of both morning and evening clock genes, implicating multiple entry points of ELF4 within the multiloop network. This could be mathematically modeled. Furthermore, morning-expression defects were particular to some elf4 alleles, suggesting predominant ELF4 action just preceding dawn. We provide a new hypothesis about ELF4 in the oscillator-it acts as a homodimer to integrate two arms of the circadian clock.

In biology, the exception is mostly the rule, and the rule is mostly the exception. However, recent results indicate that known universal concepts in biology such as the genetic code or the utilization of ATP as a source of energy may be complemented by a large class of principles based on Shannon's concept of information. The present position paper discusses various promising pathways toward the formulation of such generic informational principles and their relevance for the realm of biology.

The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We argue that progress in understanding the processes of network evolution is only possible when hypotheses are formulated as plausible evolutionary models and compared against the observed data within the framework of probabilistic modeling. The value of such models is expected to be greatly enhanced as they incorporate more of the details of the biophysical properties of interacting proteins, gene phylogeny, and measurement error and as more advanced methodologies emerge for model comparison and the inference of ancestral network states.

STDP (spike-timing-dependent synaptic plasticity) is thought to be a synaptic learning rule that embeds spike-timing information into a specific pattern of synaptic strengths in neuronal circuits, resulting in a memory. STDP consists of bidirectional long-term changes in synaptic strengths. This process includes long-term potentiation and long-term depression, which are dependent on the timing of presynaptic and postsynaptic spikings. In this review, we focus on computational aspects of signaling mechanisms that induce and maintain STDP as a key step toward the definition of a general synaptic learning rule. In addition, we discuss the temporal and spatial aspects of STDP, and the requirement of a homeostatic mechanism of STDP in vivo.

Light-responsive biologically active compounds offer the possibility to study the dynamics of biological processes. Phototriggers and photoswitches have been designed, providing the capability to rapidly cause the initiation of wide range of dynamic biological phenomena. We will discuss, in this article, recent developments in the field of light-triggered chemical tools, specially how two-photon excitation, "caged" fluorophores, and the photoregulation of protein activities in combination with time-resolved x-ray techniques should break new grounds in the understanding of dynamic biological processes.

Although previous studies suggested that the interfacial tension gamma(cc) acting along cell-cell boundaries and the effective viscosity mu of the cell cytoplasm could be measured by compressing a spherical aggregate of cells between parallel plates, the mechanical understanding necessary to extract this information from these tests-tests that have provided the surface tension sigma(cm) acting along cell-medium interfaces-has been lacking. These tensions can produce net forces at the subcellular level and give rise to cell motions and tissue reorganization, the rates of which are regulated by mu. Here, a three-dimensional (3D) cell-based finite element model provides insight into the mechanics of the compression test, where these same forces are at work, and leads to quantitative relationships from which the effective viscosity mu of the cell cytoplasm, the tension gamma(cc) that acts along internal cell-cell interfaces and the surface tension sigma(cp) along the cell-platen boundaries can be determined from force-time curves and aggregate profiles. Tests on 5-day embryonic chick mesencephalon, neural retina, liver, and heart aggregates show that all of these properties vary significantly with cell type, except gamma(cc), which is remarkably constant. These properties are crucial for understanding cell rearrangement and tissue self-organization in contexts that include embryogenesis, cancer metastases, and tissue engineering.

Collective motion of cell cultures is a process of great interest, as it occurs during morphogenesis, wound healing, and tumor metastasis. During these processes cell cultures move due to the traction forces induced by the individual cells on the surrounding matrix. A recent study [Trepat, et al. (2009). Nat. Phys. 5, 426-430] measured for the first time the traction forces driving collective cell migration and found that they arise throughout the cell culture. The leading 5-10 rows of cell do play a major role in directing the motion of the rest of the culture by having a distinct outwards traction. Fluctuations in the traction forces are an order of magnitude larger than the resultant directional traction at the culture edge and, furthermore, have an exponential distribution. Such exponential distributions are observed for the sizes of adhesion domains within cells, the traction forces produced by single cells, and even in nonbiological nonequilibrium systems, such as sheared granular materials. We discuss these observations and their implications for our understanding of cellular flows within a continuous culture.