New Scaling Relation for Information Transfer in Biological Networks

Living systems are often described utilizing informational analogies. An important open question is whether information is merely a useful conceptual metaphor, or intrinsic to the operation of biological systems. To address this question, we provide a rigorous case study of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast S. pombe and that of the budding yeast S. cerevisiae. We compare our results for these biological networks to the same analysis performed on ensembles of two different types of random networks. We show that both biological networks share features in common that are not shared by either ensemble. In particular, the biological networks in our study, on average, process more information than the random networks. They also exhibit a scaling relation in information transferred between nodes that distinguishes them from either ensemble: even when compared to the ensemble of random networks that shares important topological properties, such as a scale-free structure. We show that the most biologically distinct regime of this scaling relation is associated with the dynamics and function of the biological networks. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). These results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties

Numerical coupling of fluid and structure in cardiac flow and devices

Numerical simulations are a powerful tool in investigation of flow and structure dynamics in biological systems and in the design of biomedical devices. Time-dependent fluid-structure interaction (FSI) problems in biological systems are often characterized by a periodic nature and relatively low Reynolds number. In order to solve the dynamics of the fluid and structure of coupled systems, different approaches may be used. Several parameters such as geometrical complexity, degree of displacement, convergence to steady periodicity, and the system stability may determine the coupling method. In the talk, four numerical studies of biological and implanted systems will be presented, each with a different FSI approach. The first study is of flow through mechanical heart valves, using finite-volume (FV) fluid solver coupled with an external structural solver using a weak coupling scheme for large displacements. The second study is of flow inside a pulsatile ventricular assist device with FV fluid solver coupled with finite-element (FE) structure solver using a strong staggered coupling assuming small displacements. The third study is of flow through vulnerable plaque in the coronary arteries, with FE solvers for both the fluid and structure domains, using a fully-coupled iterative scheme assuming small displacements. The fourth simulation is of an impedance pump using a direct FE coupling method for large displacements. In addition to the methodology, the applicative design and hemodynamic aspects of the cases will be discussed, including washout properties and risk for thrombosis. The results obtained from the studies will be compared to experimental analyses

Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies

The structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.
A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.
In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics.
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From Network Structure to Dynamics and Back Again: Relating dynamical stability and connection topology in biological complex systems

The recent discovery of universal principles underlying many complex networks occurring across a wide range of length scales in the biological world has spurred physicists in trying to understand such features using techniques from statistical physics and non-linear dynamics. In this paper, we look at a few examples of biological networks to see how similar questions can come up in very different contexts. We review some of our recent work that looks at how network structure (e.g., its connection topology) can dictate the nature of its dynamics, and conversely, how dynamical considerations constrain the network structure. We also see how networks occurring in nature can evolve to modular configurations as a result of simultaneously trying to satisfy multiple structural and dynamical constraints. The resulting optimal networks possess hubs and have heterogeneous degree distribution similar to those seen in biological systems.Comment: 15 pages, 6 figures, to appear in Proceedings of "Dynamics On and Of Complex Networks", ECSS'07 Satellite Workshop, Dresden, Oct 1-5, 200

Biosynthesis, structure, and biological activities of envelope protein gp65 of murine coronavirus.

We have previously shown that gp65 (E3) is a virion structural protein which varies widely in quantity among different strains of mouse hepatitis virus (MHV). In this study, the biosynthetic pathway and possible biological activities of this protein were examined. The glycosylation of gp65 in virus-infected cells was inhibited by tunicamycin but not by monensin, suggesting that it contains an N-glycosidic linkage. Glycosylation is cotranslational and appears to be complete before the glycoprotein reaches the Golgi complex. Pulse-chase experiments showed that this protein decreased in size after 30 min of chase, suggesting that the carbohydrate chains of gp65 undergo trimming during its transport across the Golgi. This interpretation is supported by the endoglycosidase treatment of gp65, which showed that the peptide backbone of gp65 did not decrease in size after pulse-chase periods. This maturation pathway is distinct from that of the E1 or E2 glycoproteins. Partial endoglycosidase treatment indicated that gp65 contains 9 to 10 carbohydrate side chains; thus, almost all of the potential glycosylation sites of gp65 were glycosylated. In vitro translation studies coupled with protease digestion suggest that gp65 is an integral membrane protein. The presence of gp65 in the virion is correlated with the presence of an acetylesterase activity. No hemagglutinin activity was detected

Structure of catalase determined by MicroED.

MicroED is a recently developed method that uses electron diffraction for structure determination from very small three-dimensional crystals of biological material. Previously we used a series of still diffraction patterns to determine the structure of lysozyme at 2.9 Å resolution with MicroED (Shi et al., 2013). Here we present the structure of bovine liver catalase determined from a single crystal at 3.2 Å resolution by MicroED. The data were collected by continuous rotation of the sample under constant exposure and were processed and refined using standard programs for X-ray crystallography. The ability of MicroED to determine the structure of bovine liver catalase, a protein that has long resisted atomic analysis by traditional electron crystallography, demonstrates the potential of this method for structure determination