A biomolecular isolation framework for eco-systems biology

Mixed microbial communities are complex, dynamic and heterogeneous. It is therefore essential that biomolecular fractions obtained for high-throughput omic analyses are representative of single samples to facilitate meaningful data integration, analysis and modeling. We have developed a new methodological framework for the reproducible isolation of high-quality genomic DNA, large and small RNA, proteins, and polar and non-polar metabolites from single unique mixed microbial community samples. The methodology is based around reproducible cryogenic sample preservation and cell lysis. Metabolites are extracted first using organic solvents, followed by the sequential isolation of nucleic acids and proteins using chromatographic spin columns. The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods. To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest. The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.     

A holistic approach to protein docking

Docking of unbound protein structures into a complex has gained significant progress in recent years, but nonetheless still poses a great challenge. We have pursued a holistic approach to docking which brings together effective methods at different stages. First, protein-protein interaction sites are predicted or obtained from experimental studies in the literature. Interface prediction/experimental data are then used to guide the generation of docked poses or to rank docked poses generated from an unbiased search. Finally, selected models are refined by lengthy molecular dynamics (MD) simulations in explicit water. For CAPRI target T27, we used information on interaction sites as input to drive docking and as a filter to rank docked poses. Lead candidates were then clustered according to RMSD among them. From the clustering, 10 models were selected and subject to refinement by MD simulations. Our Model 7 is rated number one among all submissions according to L_rmsd. Six of our other submissions are rated acceptable. As scorer, eight of our submissions are rated acceptable.     

A holistic approach to protein structure alignment

A method of protein structure comparison developed previously is extended to incorporate other aspects of protein structure in addition to the inter-atomic vectors on which it was originally based. Each additional aspect, which induced hydrogen bonding, solvent exposure, torsional angles and sequence, was introduced separately and evaluated for its ability to improve alignment quality. The components were then combined, suitably weighted, to produce a more holistic comparison method. The method was tested on a group of remotely related beta/alpha type proteins that share a common feature in their overall chain fold. The results indicated that while the original inter-atomic vector component was sufficient to give the correct alignment of most pairs of topologically equivalent proteins, the inclusion of hydrogen bonds, torsion angles and a measure of solvent exposure led to improvements in the more difficult comparisons. Consideration of amino acid properties, including hydrophobicity, had no beneficial effect. The failure of the latter component was not unexpected considering the almost total lack of sequence similarity among the proteins considered.     

A set-theoretical approach to biology

In a preceding paper (Bull. Math. Biophysics 20, 71–93, 1958) the principle of biotopological mapping was formulated in terms of a continuous mapping of an abstract space, made from the set of biological properties which characterize the organism, by an appropriate definition of neighborhoods. In this paper it is shown that we may consider directly the mappings of the different sets of properties which characterize different organisms without taking recourse to abstract spaces. All the verificable conclusions made in the preceding paper remain valid. A serious difficulty mentioned previously is, however, avoided and the possibility of more general predictions is established.     

A sociological approach to biology

The paper develops further some suggestions made previously (Bulletin of Mathematical Biophysics,28, 283–308, 1966) that certain biological phenomena may be more easily interpreted from a “sociological” point of view by considering the organism as a social aggregate of cells and a cell as a social aggregate of genes. In this light the problems of origin of life on earth, of aging, and of parasitism and symbiosis are discussed. The notion of social aggregates of different orders is introduced.     

A holistic approach to study the temporal variability in gait

Movement variability has become an important field of research and has been studied to gain a better understanding of the neuro-muscular control of human movements. In addition to studies investigating "amplitude variability" there are a growing number of studies assessing the "temporal variability" in movements by applying non-linear analysis techniques. One limitation of the studies available to date is that they quantify variability features in specific, pre-selected biomechanical or physiological variables. In many cases it remains unclear if and to what degree these pre-selected variables quantify characteristics of the whole body movement. This technical note proposes to combine two analysis techniques that have already been applied for gait analysis in order to quantify variability features in walking with variables whose significance for the whole movements are known. Gait patterns were recorded using a full-body marker set on the subjects whose movements were captured with a standard motion tracing system. For each time frame the coordinates of all markers were interpreted as a high-dimensional "posture vector". A principal component analysis (PCA) conducted on these posture vectors identified the main one-dimensional movement components of walking. Temporal variability of gait was then quantified by calculating the maximum Lyapunov Exponent (LyE) of these main movement components. The effectiveness of this approach was demonstrated by determining differences in temporal variability between walking in unstable shoes and walking in a normal athletic-type control shoe. Several additional conceptual and practical advantages of this combination of analysis methods were discussed.     

A systems biology approach to learning autophagy

With its relevance to our understanding of eukaryotic cell function in the normal and disease state, autophagy is an important topic in modern cell biology; yet, few textbooks discuss autophagy beyond a two- or three-sentence summary. Here, we report an undergraduate/graduate class lesson for the in-depth presentation of autophagy using an active learning approach. By our method, students will work in small groups to solve problems and interpret an actual data set describing genes involved in autophagy. The problem-solving exercises and data set analysis will instill within the students a much greater understanding of the autophagy pathway than can be achieved by simple rote memorization of lecture materials; furthermore, the students will gain a general appreciation of the process by which data are interpreted and eventually formed into an understanding of a given pathway. As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single data set can be used to define the pathway in full-the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy. In total, our teaching methodology offers an effective presentation of autophagy as well as a general template for the discussion of nearly any signaling pathway within the eukaryotic kingdom.     

A chemical approach to stem cell biology

Small molecule libraries have been used successfully to probe several biological systems. Recent work has translated these successes across to the field of stem cell biology. Stem cells hold promise for both modeling of early development as well as having therapeutic potential. Enhanced understanding of the molecular mechanisms that control stem cell fates as well as an improved ability to manipulate cell populations are required. Known mechanistic chemical compounds have been used with stem cells to accomplish these two goals. More recently, through the utilization of high fitness libraries in phenotype-based screens, several small molecules that control self-renewal and differentiation in stem cells have been identified. These small molecules provide useful chemical tools for both basic research and practical applications.     

A holistic approach to ecosystem health assessment using fish population characteristics

The status of a fish population is a reflection of the overall condition of the aquatic environment in which that population resides. As such, fish population characteristics can be used as indicators of environmental health. Simple and inexpensive methods to follow fish population responses to environmental degradation are lacking. This paper outlines a protocol whereby environmental impacts on fish populations are classified by five patterns based on characteristics such as mean age, fecundity and condition factor. The patterns summarize population changes and describe responses to exploitation, recruitment failure, the presence of multiple stressors, food limitation and niche shifts. Classification is best based on the selection, and appropriate sampling, of a comparable reference population. Population characteristics can be used to examine ecosystems exposed to stressors for evidence of long-term damage, and when used with biochemical indicators, can be a powerful tool for ecosystem health assessment. The five responses are illustrated using published data on a number of species challenged by increased predation pressure, acidification, eutrophication, mine waste and reservoir impoundment. Application of this scheme will aid in directing and focusing research efforts on crucial aspects impacted by changing conditions.     

Systems biology approach to bioremediation

Bioremediation has historically been approached as a 'black box' in terms of our fundamental understanding. Thus it succeeds and fails, seldom without a complete understanding of why. Systems biology is an integrated research approach to study complex biological systems, by investigating interactions and networks at the molecular, cellular, community, and ecosystem level. The knowledge of these interactions within individual components is fundamental to understanding the dynamics of the ecosystem under investigation. Understanding and modeling functional microbial community structure and stress responses in environments at all levels have tremendous implications for our fundamental understanding of hydrobiogeochemical processes and the potential for making bioremediation breakthroughs and illuminating the 'black box'.     

A systems biology approach to identify effective cocktail drugs

Background

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

Results

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

Conclusions

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.