BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Wetting phenomena are ubiquitous in nature and technology. A solid substrate exposed to the environment is almost invariably covered by a layer of fluid material. In this review, the surface forces that lead to wetting are considered, and the equilibrium surface coverage of a substrate in contact with a drop of liquid. Depending on the nature of the surface forces involved, different scenarios for wetting phase transitions are possible; recent progress allows us to relate the critical exponents directly to the nature of the surface forces which lead to the different wetting scenarios. Thermal fluctuation effects, which can be greatly enhanced for wetting of geometrically or chemically structured substrates, and are much stronger in colloidal suspensions, modify the adsorption singularities. Macroscopic descriptions and microscopic theories have been developed to understand and predict wetting behavior relevant to microfluidics and nanofluidics applications. Then the dynamics of wetting is examined. A drop, placed on a substrate which it wets, spreads out to form a film. Conversely, a nonwetted substrate previously covered by a film dewets upon an appropriate change of system parameters. The hydrodynamics of both wetting and dewetting is influenced by the presence of the three-phase contact line separating "wet" regions from those that are either dry or covered by a microscopic film only. Recent theoretical, experimental, and numerical progress in the description of moving contact line dynamics are reviewed, and its relation to the thermodynamics of wetting is explored. In addition, recent progress on rough surfaces is surveyed. The anchoring of contact lines and contact angle hysteresis are explored resulting from surface inhomogeneities. Further, new ways to mold wetting characteristics according to technological constraints are discussed, for example, the use of patterned surfaces, surfactants, or complex fluids.

/pdf/wetting-and-spreading-56ditqbfmj.pdf

Wetting and Spreading

Jets, ie collimated streams of matter, occur from the microscale up to the large-scale structure of the universe Our focus will be mostly on surface tension effects, which result from the cohesive properties of liquids Paradoxically, cohesive forces promote the breakup of jets, widely encountered in nature, technology and basic science, for example in nuclear fission, DNA sampling, medical diagnostics, sprays, agricultural irrigation and jet engine technology Liquid jets thus serve as a paradigm for free-surface motion, hydrodynamic instability and singularity formation leading to drop breakup In addition to their practical usefulness, jets are an ideal probe for liquid properties, such as surface tension, viscosity or non-Newtonian rheology They also arise from the last but one topology change of liquid masses bursting into sprays Jet dynamics are sensitive to the turbulent or thermal excitation of the fluid, as well as to the surrounding gas or fluid medium The aim of this review is to provide a unified description of the fundamental and the technological aspects of these subjects

Physics of liquid jets

Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.

http://absimage.aps.org/image/MAR08/MWS_MAR08-2008-020581.pdf

The Genomic Code for Nucleosome Positioning

Recent advances in the understanding of the melting behavior of double-stranded DNA with statistical mechanics methods lead to improved estimates of the weight factors for the dissociation events of the chains, in particular for interior loop melting. So far, in the modeling of DNA melting, the entropy of denaturated loops has been estimated from the number of configurations of a closed self-avoiding walk. It is well understood now that a loop embedded in a chain is characterized by a loop closure exponent c which is higher than that of an isolated loop. Here we report an analysis of DNA melting curves for sequences of a broad range of lengths (from $10$ to ${10}^{6}$ base pairs) calculated with a program based on the algorithms underlying MELTSIM. Using the embedded loop exponent we find that the cooperativity parameter is one order of magnitude bigger than current estimates. We argue that in the melting region the double helix persistence length is greatly reduced compared to its room temperature value, so that the use of the embedded loop closure exponent for real DNA sequences is justified.

Reparametrizing the loop entropy weights: effect on DNA melting curves.

InAs islands on GaAs substrates undergo a morphological change into ring-shaped configurations upon deposition of a GaAs layer after island growth. By invoking an analogy of the InAs islands to wetting droplets on solid substrates, we suggest that this transition might be brought about by a change of the surface free-energy balance at the three-phase contact-line between GaAs, InAs, and vacuum (or As atmosphere), Our scenario can also be tested in conventional liquid systems (e.g., polymers).

Wetting droplet instability and quantum ring formation.

We propose a compositional approach to the dynamics of gene regu-latory networks based on the stochastic π-calculus, and develop a representation of gene network elements which can be used to build complex circuits in a transparent and efficient way. To demonstrate the power of the approach we apply it to several artificial networks, such as the repressilator and combinatorial gene circuits first studied in Combinatorial Synthesis of Genetic Networks [1]. For two examples of the latter systems, we point out how the topology of the circuits and the interplay of the stochastic gate interactions influence the circuit behavior. Our approach may be useful for the testing of biological mechanisms proposed to explain the experimentally observed circuit dynamics.

/pdf/a-compositional-approach-to-the-stochastic-dynamics-of-gene-2k4rh4rqk5.pdf

A compositional approach to the stochastic dynamics of gene networks

A remarkable change in topology occurs when In y Ga 1− y As quantum dots, grown by Stranski–Krastanov self-organization, are covered by a thin layer of GaAs. The nano-islands rearrange themselves to form volcano-like islands with distinct, ring-like features. The island topology can be preserved during overgrowth and thus used for the fabrication of ring-shaped quantum structures. The experimental data suggests that two mechanisms, diffusion and dewetting, are driving the transformation from dots to rings.

Morphological transformation of InyGa1−yAs islands, fabricated by Stranski–Krastanov growth

Providing efficient ways to model the dynamics of gene regulatory networks is an important challenge in systems biology. Many different methods have been proposed in the past for such dynamical networks. One prominent approach is based on discrete logical methods, going back to the pioneering work by Kauffman on synchronous Boolean networks (Kauffman, 1969) and Thomas on asynchronous Boolean networks (Thomas, 1973, 1991); reviews of the current state of such approaches are in De Jong, 2002, Smolen et al., 2002. A different, independent approach is based on rate equations, hence on the continuous dynamics of nonlinear ordinary differential equations (ODEs) (Goldbeter, 1996). Finally, there are various variants of stochastic methods, based either on the master equation approach (Van Kampen, 1992) or on the equivalent Gillespie algorithm (Gillespie, 1977).

The basic underlying problem for the quantitative description of the dynamics of gene regulatory networks is the enormous diversity of the “actors” involved, i.e., the biomolecules which determine the network structure and dynamics. Both from an analytic and a computational point of view, one therefore needs to simplify in order to make simulations of such networks feasible: representing all actors by individual computational elements is simply unfeasible. But this is not the only problem. Two obvious other challenges are: (i) to have flexible modeling schemes, and (ii) schemes which do not grow too fast with the increase of the number of reactions included.

In this paper we propose an approach to models of gene regulatory network dynamics which is both flexible and has such advantages in terms of system size. It combines two features:

First, our modeling approach to gene regulatory networks is based on an abstraction of the genome as a set of input-output elements, the gene gates (Blossey et al., 2006). The properties of each gate are defined by a set of abstract kinetic reactions. (In the simplest - Boolean - setting, a gate would be either on or off.) Based on these modules, regulatory circuits can be constructed by formulating input-output relationships between the gates. An advantage of this modeling approach is that it allows to start with a very simple construction of the gates to represent the overall topology of the network. We show how more biological detail can be added to the model while leaving the underlying topology of the network unaltered. The approach therefore permits to build computational models with variable degrees of detail which is highly desirable given the incomplete knowledge of most biological systems.

Second, the full advantage of our compositional approach can be seen by formulating the networks in terms of processes defined in a process calculus, the π-calculus, which originates in the field of programming languages in theoretical computer science (Milner, 1999), and has been proposed for applications to systems biology only recently (Priami et al., 2001; Regev and Shapiro, 2002; Regev, 2003). Not only do the compositional features of this calculus allow to express each gate as an autonomous network element, they also significantly reduce the system size (Cardelli, 2007). For 2n elements, the size of the input∕output interface equals 2n, while the number of kinetic reactions can be in the worst case n2. An introduction to the stochastic π-calculus and its use in simulations is presented in the Supplementary Material (see also Phillips and Cardelli, 2007).

The process calculus directly allows for a stochastic formulation of the dynamics, which is clearly more realistic for networks of molecules with small copy numbers than the deterministic dynamics. This feature can indeed be critical for the description of the network dynamics.

We illustrate this by our application of the approach to the repressilator, a three-gate inhibitory network which is an artificial cellular clock realized experimentally (Elowitz and Leibler, 2000). While the repressilator readily oscillates within a stochastic dynamics without cooperative mechanisms in the interaction between genes and transcription factors, such cooperativity is required to bring about oscillations in a deterministic (nonlinear) gate dynamics as we show by mapping the gene gate kinetics onto deterministic ODEs.

Finally, we demonstrate the refinement of the basic description to include more details of the biological system for the repressilator by an inclusion of the transcription, translation and repressor binding processes. Protein complexation is found to regularize the oscillations.

/pdf/compositionality-stochasticity-and-cooperativity-in-dynamic-25nwqwx9sr.pdf

Ralf Blossey

Papers

Reparametrizing the loop entropy weights: effect on DNA melting curves.

Wetting droplet instability and quantum ring formation.

A compositional approach to the stochastic dynamics of gene networks

Morphological transformation of InyGa1−yAs islands, fabricated by Stranski–Krastanov growth

Compositionality, Stochasticity and Cooperativity in Dynamic Models of Gene Regulation