Computational models of molecular self-organization in cellular environments

The cellular environment creates numerous obstacles to efficient chemistry, as molecular components must navigate through a complex, densely crowded, heterogeneous, and constantly changing landscape in order to function at the appropriate times and places. Such obstacles are especially challenging to self-organizing or self-assembling molecular systems, which often need to build large structures in confined environments and typically have high-order kinetics that should make them exquisitely sensitive to concentration gradients, stochastic noise, and other non-ideal reaction conditions. Yet cells nonetheless manage to maintain a finely tuned network of countless molecular assemblies constantly forming and dissolving with a robustness and efficiency generally beyond what human engineers currently can achieve under even carefully controlled conditions. Significant advances in high-throughput biochemistry and genetics have made it possible to identify many of the components and interactions of this network, but its scale and complexity will likely make it impossible to understand at a global, systems level without predictive computational models. It is thus necessary to develop a clear understanding of how the reality of cellular biochemistry differs from the ideal models classically assumed by simulation approaches and how simulation methods can be adapted to accurately reflect biochemistry in the cell, particularly for the self-organizing systems that are most sensitive to these factors. In this review, we present approaches that have been undertaken from the modeling perspective to address various ways in which self-organization in the cell differs from idealized models.     

The molecular underpinnings of genetic phenomena

Epiphenomena are those processes that ostensibly have no precedent at lower levels of scientific organization. In this review, it is argued that many genetic processes, including ploidy, dominance, heritability, pleiotropy, epistasis, mutational load and recombination, all are at least analogous to biochemical events that were requisite features of the RNA world. Most, if not all, of these features of contemporary whole organisms and populations may have their ultimate evolutionary roots in the chemical repertoire of catalytic RNAs. Some of these phenomena will eventually prove to be not only analogous but homologous to ribozyme activities.     

What's old and new in molecular phylogenetics

While it is fairly easy to devise a phylogenetic tree based on molecular data, it has proven difficult to tell how reliable any such tree is. Thus while the genetic inference that humans, chimpanzees, and gorillas cluster together is widely accepted, the genetic inference that the primary division among Old World human populations is between Asia and EurAfrica is not. A molecular phylogenetic inference linking humans and chimpanzees was proposed in the 1980s based on the technique of DNA hybridization. Despite several recent publications in primary and secondary source material, much confusion still exists surrounding the work. This paper tries to clarify issues that may still be confusing to physical anthropologists, and proposes criteria upon which to judge the robusticity of a phylogenetic inference based on DNA hybridization, in light of a recent published claim of replication. The claim of replication is considered critically. Interestingly, the original DNA hybridization data may actually show a chimp-gorilla link, in harmony with other phylogenetic results.     

Complicated behavior of G-quadruplexes and evaluating G-quadruplexes' ligands in various systems mimicking cellular circumstance

Environments surrounding G-rich sequences remarkably affect the conformations of these structures. A proper evaluation system mimicking the crowded environment in a cell with macromolecules should be developed to perform structural and functional studies on G-quadruplexes. In this study, the topology and stability of a G-quadruplex formed by human telomeric repeat sequences were investigated in a macromolecule-crowded environment created by polyethylene glycol 200 (PEG200), tumor cell extract, and Xenopus laevis egg extract. The interactions between small molecules and telomeric G-quadruplexes were also evaluated in the different systems. The results suggested that the actual behavior of G-quadruplex structures in cells extract is quite different from that in the PEG crowding system, and proteins or other factors in extracts might play a very important role in G-quadruplex structures.     

Electromagnetic cellular interactions

Chemical and electrical interaction within and between cells is well established. Just the opposite is true about cellular interactions via other physical fields. The most probable candidate for an other form of cellular interaction is the electromagnetic field. We review theories and experiments on how cells can generate and detect electromagnetic fields generally, and if the cell-generated electromagnetic field can mediate cellular interactions. We do not limit here ourselves to specialized electro-excitable cells. Rather we describe physical processes that are of a more general nature and probably present in almost every type of living cell. The spectral range included is broad; from kHz to the visible part of the electromagnetic spectrum. We show that there is a rather large number of theories on how cells can generate and detect electromagnetic fields and discuss experimental evidence on electromagnetic cellular interactions in the modern scientific literature. Although small, it is continuously accumulating.     

Peptide microarrays for the detection of molecular interactions in cellular signal transduction

The formation of protein complexes is a hallmark of cellular signal transduction. Here, we show that peptide microarrays provide a robust and quantitative means to detect signalling-dependent changes of molecular interactions. Recruitment of a protein into a complex upon stimulation of a cell leads to the masking of an otherwise exposed binding site. In cell lysates this masking can be detected by reduced binding to a microarray carrying a peptide that corresponds to the binding motif of the respective interaction domain. The method is exemplified for the lymphocyte-specific tyrosine kinase 70 kDa zeta-associated protein binding to a bis-phosphotyrosine-motif of the activated T-cell receptor via its tandem SH2 domain. Compared to established techniques, the method provides a significant shortcut to the detection of molecular interactions.     

Analysis of arthropod bloodmeals using molecular genetic markers

Little is known about the transmission dynamics of human malaria and other vector-borne diseases, partly because of the limited availability and distribution of appropriate tools for quantifying human-mosquito contact rates. Recent developments in molecular biology have allowed a significant increase in the efficacy and reliability of bloodmeal identification, and DNA-based molecular markers are now being harnessed for typing arthropod bloodmeals. The extent to which these markers have been used for analysis of mosquito bloodmeals and the potential they might have for the future is discussed, and the contributions that the advent of PCR has made are examined here.     

High-dimensional switches and the modelling of cellular differentiation

Many genes have been identified as driving cellular differentiation, but because of their complex interactions, the understanding of their collective behaviour requires mathematical modelling. Intriguingly, it has been observed in numerous developmental contexts, and particularly haematopoiesis, that genes regulating differentiation are initially co-expressed in progenitors despite their antagonism, before one is upregulated and others downregulated. We characterise conditions under which three classes of generic "master regulatory networks", modelled at the molecular level after experimentally observed interactions (including bHLH protein dimerisation), and including an arbitrary number of antagonistic components, can behave as a "multi-switch", directing differentiation in an all-or-none fashion to a specific cell-type chosen among more than two possible outcomes. bHLH dimerisation networks can readily display coexistence of many antagonistic factors when competition is low (a simple characterisation is derived). Decision-making can be forced by a transient increase in competition, which could correspond to some unexplained experimental observations related to Id proteins; the speed of response varies with the initial conditions the network is subjected to, which could explain some aspects of cell behaviour upon reprogramming. The coexistence of antagonistic factors at low levels, early in the differentiation process or in pluripotent stem cells, could be an intrinsic property of the interaction between those factors, not requiring a specific regulatory system.     

微生物对环境遗传适应和质粒的分子生态效应

微生物对环境的遗传适应是进化的机制,它与环境因子之间存在着复杂的信号网络,调节微生物与环境新奇信号（污染物质）之间的相互适应、质粒的形成与存在正是宿主细胞获得这种适应的产物,由于它能在自然环境中广泛传播,导致了物种的多样性．本文概述了微生物对环境污染物的应答和质粒的分子生态效应     

Assessment of genetic diversity among faba bean genotypes using agro-morphological and molecular markers

Forty faba bean (Vicia faba L.) genotypes were evaluated for their agro-morphological performance and molecular diversity under Central Region of Saudi Arabia conditions during 2010–11 and 2011–12 seasons. Field performance results showed that faba genotypes exhibited a significant amount of variation for their agro-morphological studied parameters. Giza40 recorded the tallest genotype (139.5 cm), highest number of seeds per plants (100.8), and the highest seed yield per plant (70.8 g). The best performing genotypes were Giza40, FLIP03-014FB, Gazira1 and Goff1. Genetic variability among genotypes was determined using Sequence Related Amplified Polymorphism (SRAP) and Amplified Fragment Length Polymorphism (AFLP) markers. A total of 183 amplified fragments (alleles) and 1758 polymorphic fragments (bands) in SRAP and 202 alleles and 716 bands in AFLP were obtained using six SRAP and four AFLP primer combinations respectively. Polymorphism information content (PIC) values for AFLP and SRAP markers were higher than 0.8, indicating the existence of a considerable amount of genetic diversity among faba tested genotypes. The UPGMA based clustering of faba genotypes was largely based on origin and/or genetic background. Result of cluster analysis based on SRAP showed weak and not significant correlation while, it was highly significant based on AFLP analysis with agro-morphological characters (r = 0.01, p > 0.54 and r = 0.26, p < 0.004 respectively). Combined SRAP and AFLP markers proved to be significantly useful for genetic diversity assessment at molecular level. They exhibited high discrimination power, and were able to distinguish the faba bean genotypes with high efficiency and accuracy levels.     

Advances in the development of molecular genetic tools for Mycobacterium tuberculosis

Mycobacterium tuberculosis, a Gram-positive bacterium of great clinical relevance, is a lethal pathogen owing to its complex physiological characteristics and development of drug resistance. Several molecular genetic tools have been developed in the past few decades to study this microorganism. These tools have been instrumental in understanding how M. tuberculosis became a successful pathogen. Advanced molecular genetic tools have played a significant role in exploring the complex pathways involved in M. tuberculosis pathogenesis. Here, we review various molecular genetic tools used in the study of M. tuberculosis. Further, we discuss the applications of clustered regularly interspaced short palindromic repeat interference (CRISPRi), a novel technology recently applied in M. tuberculosis research to study target gene functions. Finally, prospective outcomes of the applications of molecular techniques in the field of M. tuberculosis genetic research are also discussed.     

Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations

The underlying molecular mechanisms of metabolic and genetic regulations are computationally identical and can be described by a finite state Markov process. We establish a common computational model for both regulations based on the stationary distribution of the Markov process with the aim of establishing a unified, quantitative model of general biological regulations. Various existing results regarding intracellular regulations are derived including the classical Michaelis-Menten equation and its generalization to more complex allosteric enzymes in a systematic way. The notion of probability flow is introduced to distinguish the equilibrium stationary distribution from the non-equilibrium one; it plays a crucial role in the analysis of stationary state equations. A graphical criterion to guarantee the existence of an equilibrium stationary distribution is derived, which turns out to be identical to the classical Wegscheider condition. Simple graphical methods to compute the equilibrium and non-equilibrium stationary distributions are derived based crucially on the probability flow, which dramatically simplifies the classical methods still used in enzymology.     

Recent advances in genetic research on schizophrenia

Evidence for genetic factors in schizophrenia is reviewed with regard to family, twin and adoption studies, and recent advances in molecular genetic technology are applied to explore possible gene loci susceptible to schizophrenia. Application of neuropsychological and neuroimaging methodologies are also reviewed with an aim to develop criteria for defining phenotypes for genetic studies.Plenary Session, Twelfth Joint Annual Conference of Biomedical Sciences, April 20, 1997, Taipei, Taiwan.     

Molecular and cellular pathophysiology of autosomal recessive polycystic kidney disease (ARPKD)

Autosomal recessive polycystic kidney disease (ARPKD) belongs to a group of congenital hepatorenal fibrocystic syndromes characterized by dual renal and hepatic involvement of variable severity. Despite the wide clinical spectrum of ARPKD (MIM 263200), genetic linkage studies indicate that mutations at a single locus, PKHD1 (polycystic kidney and hepatic disease 1), located on human chromosome region 6p21.1–p12, are responsible for all phenotypes of ARPKD. Identification of cystic disease genes and their encoded proteins has provided investigators with critical tools to begin to unravel the molecular and cellular mechanisms of PKD. PKD cystic epithelia share common phenotypic abnormalities despite the different genetic mutations that underlie the disease. Recent studies have shown that many cyst-causing proteins are expressed in multimeric complexes at distinct subcellular locations within epithelia. This co-expression of cystoproteins suggests that cyst formation, regardless of the underlying disease gene, results from perturbations in convergent and/or integrated signal transduction pathways. To date, no specific therapies are in clinical use for ameliorating cyst growth in ARPKD. However, studies noted in this review suggest that therapeutic targeting of the cAMP and epidermal growth factor receptor (EGFR)-axis abnormalities in cystic epithelia may translate into effective therapies for ARPKD and, by analogy, autosomal dominant polycystic kidney disease (ADPKD). A particularly promising approach appears to be the targeting of downstream intermediates of both the cAMP and EGFR axis. This review focuses on ARPKD and presents a concise summary of the current understanding of the molecular genetics and cellular pathophysiology of this disease. It also highlights phenotypic and mechanistic similarities between ARPKD and ADPKD.The authors are supported by the National Institutes of Health (grant no. 1-P50-DK57306), the PKD Foundation (grant no. 76a2r), and the Children’s Research Institute, Children’s Hospital of Wisconsin.     

Designing convergent cellular automata

Cellular automata (CA) have been used by biologists to study dynamic non-linear systems where the interaction between cell behaviour and end-pattern is investigated. It is difficult to achieve convergence of a CA towards a specific static pattern and a common solution is to use genetic algorithms and evolve a ruleset that describes cell behaviour. This paper presents an alternative means of designing CA to converge to specific static patterns. A matrix model is introduced and analysed then a design algorithm is demonstrated. The algorithm is significantly less computationally intensive than equivalent evolutionary algorithms, and not limited in scale, complexity or number of dimensions.     

分子遗传标记技术在中药材鉴定中的应用

分子遗传标记技术在中药材品种鉴别中的应用是中药现代化研究的一个重要方面,本文回顾国内外中药分子标记鉴定研究的历史及现状,分析了目前分子标记在中药鉴定应用中存在的问题并提出建议,进而展望这项技术应用的前景。中药材质量的规范化,为中药材遗传图谱的建立指明方向。     

A molecular genetic map of cassava (Manihot esculenta Crantz)

 A genetic linkage map of cassava has been constructed with 132 RFLPs, 30 RAPDs, 3 microsatellites, and 3 isoenzyme markers segregating from the heterozygous female parent of an intraspecific cross. The F₁ cross was made between ‘TMS 30572’ and ‘CM 2177-2’, elite cassava cultivars from Nigeria and Colombia, respectively. The map consists of 20 linkage groups spanning 931.6 cM or an estimated 60% of the cassava genome. Average marker density is 1 per 7.9 cM. Since the mapping population is an F₁ cross between heterozygous parents, with unique alleles segregating from either parent, a second map was constructed from the segregation of 107 RFLPs, 50 RAPDs, 1 microsatellite, and 1 isoenzyme marker from the male parent. Comparison of intervals in the male-and female-derived maps, bounded by markers heterozygous in both parents, revealed significantly less meiotic recombination in the gametes of the female than in the male parent. Six pairs of duplicated loci were detected by low-copy genomic and cDNA sequences used as probes. Efforts are underway to saturate the cassava map with additional markers, to join the male- and female-derived maps, and to elucidate genome organization in cassava. Received: 5 July 1996/Accepted: 22 November 1996     

Automatic reconstruction of molecular and genetic networks from discrete time series data

We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.