Hasil untuk "q-bio.MN"

Yue Wang

In some patients of myeloproliferative neoplasm, two genetic mutations can be found: JAK2 V617F and TET2. When one mutation is present or not, the other mutation has different effects on regulating gene expressions. Besides, when both mutations are present, the order of occurrence might make a difference. In this paper, we build nonlinear ordinary differential equation models and Markov chain models to explain such phenomena.

Jack Kelly, Carlo Berzuini, Bernard Keavney et al.

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology and genetics. This paper aims to introduce and review the available methods for building large-scale causal molecular networks that have been developed in the past decade. Existing methods have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also aims to discuss some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

J. C. Phillips

The titled subject has attracted much interest. Here we summarize the substantial results obtained by a physical model of protein evolution based on hydropathic domain dynamics. In a recent Letter eighteen biologists suggested that the titled subject should be studied in a way inclusive of broad expertise (1). There is an even broader view that has been developed over several decades by physicists (2,3). This view is based on analyzing amino acid sequences of proteins. These sequences are now available on-line at Uniprot, and represent a treasure-trove of data (4).

Linar Mikeev, Werner Sandmann

Numerical solution of the chemical master equation for stochastic reaction networks typically suffers from the state space explosion problem due to the curse of dimensionality and from stiffness due to multiple time scales. The dimension of the state space equals the number of molecular species involved in the reaction network and the size of the system of differential equations equals the number of states in the corresponding continuous-time Markov chain, which is usually enormously huge and often even infinite. Thus, efficient numerical solution approaches must be able to handle huge, possibly infinite and stiff systems of differential equations efficiently. We present an approximate numerical integration approach that combines a dynamical state space truncation procedure with efficient numerical integration schemes for systems of ordinary differential equations including adaptive step size selection based on local error estimates. The efficiency and accuracy is demonstrated by numerical examples.

Fabrizio Pucci, Alexander Schug

Structured RNA plays many functionally relevant roles in molecular life. Structural information, while required to understand the functional cycles in detail, is challenging to gather. Computational methods promise to complement experimental efforts by predicting three-dimensional RNA models. Here, we provide a concise view of the state of the art methodologies with a focus on the strengths and the weaknesses of the different approaches. Furthermore, we analyzed the recent developments regarding the use of coevolutionary information and how it can boost the prediction performances. We finally discuss some open perspectives and challenges for the near future in the RNA structural stability field.

Arnab Barua

In this paper I have given a mathematical model of Cell reprogramming from a different contexts. Here I considered there is a delay in differential regulator rate equations due to intermediate regulator's regulations. At first I gave some basic mathematical models by Ferell Jr.[2] of reprogramming and after that I gave mathematical model of cell reprogramming by Mithun Mitra[4]. In the last section I contributed a mathematical model of cell reprogramming from intermediate steps regulations and tried to find the critical point of pluripotent cell.

Timothy D. Cummins, Gopal P. Sapkota

Identification and characterization of large protein complexes is a mainstay of biochemical toolboxes. Utilization of cross-linking chemicals can facilitate the capture and identification of transient or weak interactions of a transient nature. Here we describe a detailed methodology for cell culture based proteomic approach. We describe the generation of cells stably expressing green fluorescent protein (GFP)- tagged proteins under the tetracycline-inducible promoter and subsequent proteomic analysis of GFP-interacting proteins. We include a list of proteins that were identified as interactors of GFP.

J. M. Deutsch

We study genetic networks that produce many species of non-coding RNA molecules that are present at a moderate density, as typically exists in the cell. The associations of the many species of these RNA are modeled physically, taking into account the equilibrium constants between bound and unbound states. By including the pair-wise binding of the many RNA species, the network becomes highly interconnected and shows different properties than the usual type of genetic network. It shows much more robustness to mutation, and also rapid evolutionary adaptation in an environment that oscillates in time. This provides a possible explanation for the weak evolutionary constraints seen in much of the non-coding RNA that has been studied.

Mo Deng, Amin Emad, Olgica Milenkovic

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered.

Jaewook Joo

NF-κB, one of key regulators of inflammation, apoptosis, and differentiation, was found to have noisy oscillatory shuttling between the nucleus and the cytoplasm in single cells when cells are stimulated by cytokine TNFα. We present the analytical analysis which uncovers the underlying physical mechanisms of this spectacular noise-induced transition in biological networks. Starting with the master equation describing both signaling and transcription events in NF-κB signaling network, we derived the macroscopic and the Fokker-Planck equations by using van Kampen's sysem size expansion. Using the noise-induced oscillatory signatures present in the power spectrum, we constructed the two-dimensional phase diagram where the noise-induced oscillation emerges in the dynamically stable parameter space.

Nilou Ataie

Enzymes are on the front lines of evolution. All living organisms rely on highly efficient, specific enzymes for growth, sustenance, and reproduction; and many diseases are a consequence of a mutation on an enzyme that affects its catalytic function. It follows that the function of an enzyme affects the fitness of an organism, but just as rightfully true, the function of an enzyme affects the fitness of itself. Understanding how the complexity of enzyme structure relates to its essential function will unveil the fundamental mechanisms of evolution, and, perhaps, shed light on strategies used by ancient replicators. This paper presents evidence that supports the hypothesis that enzymes, and proteins in general, are the manifestation of the coevolution of two opposing forces. The synthesis of enzyme architecture, stability, function, evolutionary relationships, and evolvability shows that the complexity of macromolecules is a consequence of the function it provides.

Mark Lipson

The use of mathematical models has helped to shed light on countless phenomena in chemistry and biology. Often, though, one finds that systems of interest in these fields are dauntingly complex. In this paper, we attempt to synthesize and expand upon the body of mathematical results pertaining to the theory of multiple equilibria in chemical reaction networks (CRNs), which has yielded surprising insights with minimal computational effort. Our central focus is a recent, cycle-based theorem by Gheorghe Craciun and Martin Feinberg, which is of significant interest in its own right and also serves, in a somewhat restated form, as the basis for a number of fruitful connections among related results.

David F. Anderson

Chemical reaction systems with a low to moderate number of molecules are typically modeled as discrete jump Markov processes. These systems are oftentimes simulated with methods that produce statistically exact sample paths such as the Gillespie Algorithm or the Next Reaction Method. In this paper we make explicit use of the fact that the initiation times of the reactions can be represented as the firing times of independent, unit rate Poisson processes with internal times given by integrated propensity functions. Using this representation we derive a modified Next Reaction Method and, in a way that achieves efficiency over existing approaches for exact simulation, extend it to systems with time dependent propensities as well as to systems with delays.

Yurie Okabe, Yuu Yagi, Masaki Sasai

A dynamical mean-field theory is developed to analyze stochastic single-cell dynamics of gene expression. By explicitly taking account of nonequilibrium and nonadiabatic features of the DNA state fluctuation, two-time correlation functions and response functions of single-cell dynamics are derived. The method is applied to a self-regulating gene to predict a rich variety of dynamical phenomena such as anomalous increase of relaxation time and oscillatory decay of correlations. Effective "temperature" defined as the ratio of the correlation to the response in the protein number is small when the DNA state change is frequent, while it grows large when the DNA state change is infrequent, indicating the strong enhancement of noise in the latter case.

Gasper Tkacik, William Bialek

The precision of biochemical signaling is limited by randomness in the diffusive arrival of molecules at their targets. For proteins binding to the specific sites on the DNA and regulating transcription, the ability of the proteins to diffuse in one dimension by sliding along the length of the DNA, in addition to their diffusion in bulk solution, would seem to generate a larger target for DNA binding, consequently reducing the noise in the occupancy of the regulatory site. Here we show that this effect is largely cancelled by the enhanced temporal correlations in one dimensional diffusion. With realistic parameters, sliding along DNA has surprisingly little effect on the physical limits to the precision of transcriptional regulation.

David Angeli, Eduardo D. Sontag

This paper studies the emergence of multi-stability and hysteresis in those systems that arise, under positive feedback, starting from monotone systems with well-defined steady-state responses. Such feedback configurations appear routinely in several fields of application, and especially in biology. Characterizations of global stability behavior are stated in terms of easily checkable graphical conditions. An example of a signaling cascade under positive feedback is presented.

Petter Holme, Mikael Huss

We propose a general method to predict functions of vertices where: 1. The wiring of the network is somehow related to the vertex functionality. 2. A fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme is tested on model networks were the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

Dagmar Iber, Joanna Clarkson, Michael D Yudkin et al.

Sporulation in Bacillus subtilis serves as a paradigm for the development of two different cell types (mother cell and prespore) from a single cell. The mechanism by which the two different developmental programs are initiated has been much studied but is not well understood. With the help of existing and new experimental results, a mathematical model has been developed that reproduces all published in vitro experiments and makes new predictions about the properties of the system in vivo.

Lutz Brusch, Gianaurelio Cuniberti, Martin Bertau

Anaerobic glycolysis in yeast perturbed by the reduction of xenobiotic ketones is studied numerically in two models which possess the same topology but different levels of complexity. By comparing both models' predictions for concentrations and fluxes as well as steady or oscillatory temporal behavior we answer the question what phenomena require what kind of minimum model abstraction. While mean concentrations and fluxes are predicted in agreement by both models we observe different domains of oscillatory behavior in parameter space. Generic properties of the glycolytic response to ketones are discussed.

Vilmos Agoston, Peter Csermely, Sandor Pongor

Robust systems, like the molecular networks of living cells are often resistant to single hits such as those caused by high-specificity drugs. Here we show that partial weakening of the Escherichia coli and Saccharomyces cerevisiae transcriptional regulatory networks at a small number (3-5) selected nodes can have a greater impact than the complete elimination of a single selected node. In both cases, the targeted nodes have the greatest possible impact; still the results suggest that in some cases broad specificity compounds or multitarget drug therapies may be more effective than individual high-affinity, high-specificity ones. Multiple but partial attacks mimic well a number of in vivo scenarios and may be useful in the efficient modification of other complex systems.