Журнал компьютерных наук и системной биологии

The medical profession adopts new medical technologies which are deemed necessary to diagnose and treat disease. The current range of technologies allows the clinician to make diagnostic conclusions although the scope, limitations and costs of such diagnostic techniques are often ignored. The aim of this review article is to consider the techniques currently used to diagnose health and to highlight a novel and innovative way of screening the health of the population using a non-invasive cognitive technique. In recent years there has been the emergence of many new medical technologies which adopt vague principles yet instead of reducing the cost of diagnosing disease, each new test adds to the complexity and cost of the healthcare system. In addition, each new test invariably introduces limitations/errors which can influence the test outcomes eg. (i) the genetic profile associated with diabetes differs according to the racial origins of each patient, (ii) diseases are often multi-systemic, therefore the association of one biomarker as a diagnostic indication may be inherently flawed, (iii) genetic screening is not fully reproducible due to profiling errors and (iv) the significance of phenotype is often overlooked. Researchers are working on ways to provide a single genetic screening test which can provide a complete medical diagnosis, companies are working on ways to diagnose state of health using an iPhone, etc. The approaches are invariably innovative yet may lack a fundamental scientific principle which can provide an accurate and reliable diagnostic indication. There is a need for technologies which improve the accuracy of diagnosis, overcome the limitations of current tests, and reduce the cost of diagnosing and treating disease. This article reviews one such technology: Virtual Scanning. Virtual Scanning is based upon three observations: (i) that sense perception, and in particular colour perception, is linked to the function of the autonomic nervous system; (ii) that autonomic dysfunction influences the coherent and synchronised function of the organ networks and ultimately affects cellular and molecular biology; and (iii) the stability of the autonomic nervous system is neurally regulated. Accordingly measurements of colour perception can be used with diagnostic effect and knowledge of the structure of the autonomic nervous system and physiological systems may be used to re-establish the stability of the autonomic nervous system. In summary, the author presents a number of case studies to illustrate the potential scope for Virtual Scanning. In particular how it can be used to diagnose and treat disease.

Biological complexity at a molecular and physiological level is dynamically translucent and requires a systemwide computational approach to possibly elucidate underlying mechanisms for medical and public health applications. Functional dynamics is ideal to study molecular functions given biological functions are dependent on the dynamic nature of networks it operates within. However, environmental factors significantly affect the molecular dynamics in biology, which still needs to be incorporated in study of functions for medical applicability. Through technological innovation medicine is seeing a potential shift in demand for personalized interventions, which has not been fully realized yet. Also the applicability of functional dynamics’ utility seems not visible in healthcare systems. This article addresses the above mentioned issues, challenges in translation/implementation using the example of the “virtual patient” developed through the pilot EU flagship project ICT Future of Medicine, and provides possible solutions and insights of new and existing scientific data, infrastructures and frameworks like the Learning-Adapting-Leveling model to make it feasible including policy-wise by incorporating best practice guidelines developed through the Public Health Genomics European Network and tries to touch upon its consequential impact. As a result, we see that real time integration in healthcare requires early-on involvement of all stakeholders as well as taking into account health policy issues, which is addressed by the proposed Learning-Adapting-Leveling model and the best practice guidelines. Furthermore, environmental factors and exposome properties need to be taken into consideration, which the pilot ICT Future of Medicine has been taken into account. We now possibly see a shift from stratified medicine through personalized medicine and possibly towards individualized medicine. This coupling of the pilot project ICT Future of Medicine by integrating the Learning-Adapting-Leveling model to resolve real-time integration issues and considering policy-wise the best practice guidelines has set the stage for it to potentially revolutionize the healthcare system as a whole.

This study reports on the structural and functional basis of pyrazinamide (PZA) resistance conferred by a novel mutation Ala102Pro in pncA gene as sequenced from a PZA resistant Mycobacterium tuberculosis strain. Molecular modeling studies of Wild Type (WT) and Mutant Type (MT) of Pyrazinamidase (PZase) showed the mutation at Ala102Pro does not impact on the conformation of the protein. However, the docking studies infer that MT has a higher inhibitory constant (Ki-990.0m) compared to WT (Ki-822.42m), which is indicative of drug resistance in MT. Furthermore, molecular interaction studies also reveal that WT forms 4 hydrogen bonds involved in PZA-WT Inhibitory interactions, whereas, in case of MT, it formed 5 hydrogen bonds with PZA. However, Ala102 in WT was found to be less fluctuating and more stable in Molecular dynamics Simulation when compared to Pro102 in MT which was highly fluctuating and unstable. This implies that Ala102 shall be a key residue involved in PZA inhibitory interactions. Moreover, MT does not show hydrogen bonding with PZA with Pro102 and also deviating in terms of PZA binding pose in comparison with WT. Hence, the observed deviations in terms of MT-PZA interactions shall be attributed to the drug resistance conferred.

Molecular interaction networks have emerged as central analysis concept for Omics profile interpretation. This fact is driven by the need for improving hypothesis generation beyond the mere interpretation of molecular feature lists derived from statistical analysis of high throughput experiments. A number of human gene and protein interaction networks are available for such task, but these differ with respect to biological nature of interactions represented, and vary with respect to coverage of molecular feature space on the gene, transcript, protein and metabolite level. Naturally, both elements impose major impact on hypothesis generation. We here present a methodology for deriving expanded interaction networks via consolidating available interaction information and further adding computationally inferred interactions.
Integrating interaction data as provided in the public domain repositories IntAct, BioGrid and Reactome resulted in a core interaction network representing 11,162 human protein coding genes (out of a total of 19,980 protein coding genes) and 145,391 interactions. Utilizing annotation from ontologies on involvement in specific molecular pathways and function, combined with structural (domain) information as gene/protein node parameterization allowed computation of probabilities for additional interactions resting on the information content of individual sources. Utilizing topological information as degree centrality, global clustering coefficient and characteristic path length allowed defining a cutoff for interaction probabilities, resulting in an expanded interaction network holding 13,730 protein coding genes and 830,470 interactions. Evaluating such hybrid network against established interaction networks as KEGG showed significant recovery of evident interactions, indicating the validity of the expansion methodology.
Integrating available interaction data, further enlarged by inferred interactions, provided an expanded human interactome regarding both, number of represented molecular features as well as number of interactions, thereby promising improved Omics profile interpretation.

Graph drawing is one of the imperative techniques for understanding biological regulations in a cell or among cells at the pathway level. Dynamic modeling and simulation of signaling pathways is a fundamental issue in systems biology and has growing attention from researchers with experimental or theoretical background. Here is an attempt to develop a reliable method for the analysis and modeling of specific signaling pathways contributing for the endotoxin clearance. Here, integration of proteins in signaling pathways was analyzed, which enables the understanding of complex cellular processes. In pathway drawing, location information is essential for its comprehension. However, complex shapes need to be taken into account when torus-shaped location information such as nuclear inner membrane, nuclear outer membrane, and plasma membrane is considered. Schwartz introduced the notion of crosstalk, referring to the case that two inputs work through distinct signaling pathways but cooperate to regulate cell signaling. In disquiet to that current study hypothesized at adopting electronic circuit block diagrams for the analysis of pathways contribute for endotoxin neutralization, by developing a logical circuit models and simulate using Very High Speed Integrated Circuit Hardware Description Language (VHDL), here mathematical models are developed as a viaduct between pathway model and circuit model. This method is reliable since it helps calculating the flux at each node. The method establishes that the multiple signaling pathways do not have the additive effect but the signals will cancel at the converging points. The established method can be applied for any network modeling and calculation of signal flux from different nodes to nucleus, degree of error depends on pathway modeling since it is a manual process errors in modeling can reflect on method reliability.

This paper proposes few genetic operators to obtain better alignments of multiple molecular sequences.
Datasets from DNA families of Canis familiaris dataset have been considered for experimental work and analysis. All the proposed operators in the method have been implemented and validated within a self developed software tool which allows the user to select the various genetic operators for crossover, mutation, fitness calculation, population initialization. It guarantees the next generation of populations with better fitness value. Improvement in the overall population fitness is also calculated and evaluated. Survival of the fittest policy is followed to arrive at a better fitness in following generations. Observations based on variable parameters have been recorded, analyzed and presented in the form of results. Results were also compared with few standard existing online tools to study the feasibility of the proposed operators

Automatic programming algorithms have often looked towards biology to provide inspiration for how agents can learn to solve problems. Evolutionary and swarm based methods in particular have shown great promise in how this objective can be achieved. We present Grammatical Herding, a new fitness-based automatic programming algorithm based on a simple set of rules inspired by the herd movements of horses. In this paper, we establish the design of the new algorithm and test it against a standard benchmark problem, the Santa Fe Trail.