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A comprehensive overview of various omics techniques to understand the biological responsive to engineered biomaterials is provided. Specifically, the role of omics-based approaches in designing next-generation of bioresponsive materials for various regenerative medicine approaches, as well as future outlook on integrated multi-omics platform are discussed. [Display omitted] Recent developments in biomedical engineering have focused on designing smart and bio-responsive materials. However, a critical step in designing this next generation of biomaterials is to evaluate their holistic cellular and molecular interactions. Recent technological advances in biology, specifically in omics techniques, can provide high-throughput functional readouts of the cell genome, epigenome, transcriptome, proteome and metabolome. The ability of omics-based approaches to predict the biological response to engineered biomaterials have the potential to revolutionize biomedical research and replace the traditional "trial and error" approach as the first step in designing the next-generation of biomaterials. In addition, these omics-based approaches can be used to gain insight into in vivo responses of biomaterials, which currently relies heavily on semi-quantitative imaging-based methodologies rather than rigorous computational approaches. In this review, we have outlined various omics techniques that have been utilized to quantify and understand the biological performance of engineered biomaterials. Additionally, we have critically discussed the role of omics-based approaches in designing new biomaterials for regenerative medicine, immune engineering, and drug delivery. Finally, the potential of integrated multi-omics approaches to build a holistic understanding of the biological responses to newly developed biomaterials have been discussed. [ABSTRACT FROM AUTHOR]

Pattern detection and string matching are fundamental problems in computer science and the accelerated expansion of bioinformatics and computational biology have made them a core topic for both disciplines. The requirement for computational tools for genomic analyses, such as sequence alignment, is very important, although, in most cases the resources and computational power required are enormous. The presented Multiple Genome Analytics Framework combines data structures and algorithms, specifically built for text mining and (repeated) pattern detection, that can help to efficiently address several computational biology and bioinformatics problems, concurrently, with minimal resources. A single execution of advanced algorithms, with space and time complexity O (n log n) , is enough to acquire knowledge on all repeated patterns that exist in multiple genome sequences and this information can be used as input by meta-algorithms for further meta-analyses. For the proof of concept and technology of the proposed Framework scalability, agility and efficiency, a publicly available dataset of more than 300,000 SARS-CoV-2 genome sequences from the National Center for Biotechnology Information has been used for the detection of all repeated patterns. These results have been used by newly introduced algorithms to provide answers to questions such as common patterns among all variants, sequence alignment, palindromes and tandem repeats detection, different organism genome comparisons, polymerase chain reaction primers detection, etc. • MuGA Framework can efficiently address several bioinformatics problems concurrently • MuGA Framework has application on multiple genomic sequences regardless type/origin • Computational complexity is limited to log-linear with limited resources • Applications in palindromes/tandem repeats, organism comparisons, primers detection • Proof of concept with a massive SARS-CoV-2 dataset of 300,000 variants [ABSTRACT FROM AUTHOR]

This paper presents the methods that have participated in the SHREC 2022 contest on protein–ligand binding site recognition. The prediction of protein- ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem). [Display omitted] • A new contest for binding site detection in a protein. • Proteins are provided both as molecular surfaces and in anonymized PQR format. • Analysis of computational methods for recognizing ligand binding sites in a protein. • Analysis of the performances of the methods that participated in SHREC 2022. [ABSTRACT FROM AUTHOR]

• The convergence of mathematical modeling, nanotechnology, and epidemiology signifies a genomic paradigm shift, enhancing our understanding of infectious diseases at the molecular level. • Mathematical models become precision instruments, shedding light on the intricate molecular dynamics of viral transmission, and providing crucial insights into gene regulation and expression. • Interdisciplinary collaboration, merging genomic precision with mathematical rigor, biology, and epidemiology, promises accelerated discoveries in molecular biology, fostering innovation and exploration in understanding gene regulation and expression during infectious processes. • Global collaboration, bolstered by the predictive power of molecularly-informed mathematical models and nanotechnology, becomes imperative for a comprehensive genomic response to emerging infectious diseases. The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression. [ABSTRACT FROM AUTHOR]

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS. [Display omitted] • Innovative Inverted Virtual Screening (IVS) pipeline. • Automated analysis of protein-ligand interactions. • Integrates Modeller, AlphaFold, Gromacs, and more. • Versatile for blind docking and site-specific targeting. • Modular design for custom research needs. [ABSTRACT FROM AUTHOR]

[Display omitted] • The state-of-the-art of 3D bioprinting is comprehensively reviewed with emphasis on design and processing aspects. • Major applications in the biomedical area, including the repair of bone, soft tissues and organs, are considered. • The current limitations and the latest evolutions of bioprinting-based strategies are examined. • Existing results and new horizons are discussed to provide a picture being useful to both senior and early-stage researchers. 3D bioprinting techniques have emerged as a flexible tool in tissue engineering and regenerative medicine to fabricate or pattern functional 3D bio-structures with precise geometric designs, bridging the divergence between engineered and natural tissue constructs. A significantly increasing development has been achieved in understanding the relationship between the 3D-printing process and the structures, properties, and applications of the objects created. The ongoing advancement of novel biomaterial inks has enabled manufacturing of models and in vitro implants capable of achieving some level of success in preclinical trials. Remarkable progress in cell biology and biology-inspired computational design has assisted in achieving the latest milestone with planned tissue- or organ-like constructs having specific levels of functionality. However, biofabricated constructs still have a long way to go before reaching clinics. This review presents a picture of 3D bioprinting in the context of tissue engineering and regenerative medicine, with focus on biomaterials-related and design-centred aspects. Biomedical applications are described in detail in relation to major tissues or organs considered in the human body. Current technical limitations, challenges, future prospects and improvements are critically outlined and discussed. [ABSTRACT FROM AUTHOR]

Carbohydrate binding modules (CBMs) are independent non-catalytic domains widely found in carbohydrate-active enzymes (CAZymes), and they play an essential role in the substrate binding process of CAZymes by guiding the appended catalytic modules to the target substrates. Owing to their precise recognition and selective affinity for different substrates, CBMs have received increasing research attention over the past few decades. To date, CBMs from different origins have formed a large number of families that show a variety of substrate types, structural features, and ligand recognition mechanisms. Moreover, through the modification of specific sites of CBMs and the fusion of heterologous CBMs with catalytic domains, improved enzymatic properties and catalytic patterns of numerous CAZymes have been achieved. Based on cutting-edge technologies in computational biology, gene editing, and protein engineering, CBMs as auxiliary components have become portable and efficient tools for the evolution and application of CAZymes. With the aim to provide a theoretical reference for the functional research, rational design, and targeted utilization of novel CBMs in the future, we systematically reviewed the function-related characteristics and potentials of CAZyme-derived CBMs in this review, including substrate recognition and binding mechanisms, non-catalytic contributions to enzyme performances, module modifications, and innovative applications in various fields. • Carbohydrate binding modules (CBMs) are essential for ligand recognition and specific binding. • The dominant interactions between CBMs and ligands are summarized. • CBMs are portable accessories to improve the properties of carbohydrate-active enzymes. • The novel processing methods and biochemical applications of CBMs are reviewed. [ABSTRACT FROM AUTHOR]

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research. [ABSTRACT FROM AUTHOR]

• Exposure to a 100 % H 2 atmosphere induces metabolic arrest and stops growth. • Addition of O 2 or air completely restores the H 2 induced metabolic arrest. • Under 100 % H 2 atmosphere, microalgae tolerate extreme stress situations. • Exposure to H 2 strongly decreases the fluctuations of protein residues. • In H 2 atmosphere biological products, like fruits, preserved unchanged for months. Metabolism is the sum of all chemical reactions that sustain life. There is an ongoing effort to control metabolic rate, which correlates with the maximum lifespan potential and constitutes one of the oldest scientific questions. Herein, we report on the complete reversible arrest of cellular metabolism and cell growth in a series of organisms, from microalgae to yeast upon exposure to a 100 % hydrogen atmosphere. We also report a tolerance of the microalgae under these conditions against extreme stress conditions, like high salt concentrations. The addition of oxygen or air almost completely restores the metabolic rate and cell growth. Molecular dynamics simulations are employed to decipher this phenomenon at atomic scale. Various proteins, including photosynthetic and respiratory complexes (LHCII, cytochrome c5) are probed in the interaction with hydrogen. Exposure to hydrogen, as opposed to oxygen, decreases the fluctuations of protein residues indicating thermostability. According to the above mechanism, an absolute hydrogen atmosphere can preserve biological products (e.g. fruits) for a long time without consuming any energy. By combining biological, chemical and computational methods, in this research we provide the basis for future innovative studies and advances in the field of biotechnology. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nano-Bio interaction assessment is important for implication of nanomaterials. • Nanoinformatics as an in silico tools emphasize accurate hazard and risk assessment forecasts for nanomaterials. • Nanotoxicity prediction computational tools like molecular docking, QSAR has been reviewed. • Implication of new computational tools like Artificial intelligence is the future of nanotoxicology. In recent decade, nanotechnology has got an extensive advancement in terms of production and application of nanomaterials. With the advancement, concern has risen for their biomedical and ecological safety, provoking a detailed analysis of the safety assement. Numerous experimental and computational approach has been developed to accomplish the goal of safety assessment of nanomaterials leading to orgin of interdisciplinary fields like nanoinformatics. Nanoinformatics has accomplished significant strides with the development of several modeling frameworks, data platforms, knowledge infrastructures, and in silico tools for risk assessment forecasts of nanomaterials. This review is an attemption to decipher and establish the bridge between the two emerging scientific arenas that includes computational modeling and nanotoxicity. We have reviewed the recent informations to uncover the link between the computational toxicology and nanotoxicology in terms of biomedical and ecological applications. In addition to the details about nanomaterials interaction with the biological system, this article offers a concise evaluation of recent developments in the various nanoinformatics domains. In detail, the computational tools like molecular docking, QSAR, etc. for the prediction of nanotoxicity here have been described. Moreover, techniques like molecular dynamics simulations used for experimental data collection and their translation to standard computational formats are explored. [ABSTRACT FROM AUTHOR]

• Terpenoids are a vast and diverse class of compounds with applications in both the industrial and human health spaces. • Computational biology and gene discovery enable the biosynthesis of novel terpenoids with microbial engineering. • The heterologous expression of plant-derived enzymes in microorganisms facilitates the production of complex terpenoid derivatives. • Host engineering has enabled monumental increases in the production titers of varying terpenoids. Terpenoids are a vast and diverse class of molecules with industrial and medicinal importance. The majority of these molecules are produced across kingdom Plantae via specialized metabolism. Microorganisms, mainly Escherichia coli and Saccharomyces cerevisiae, have become choice platforms for the biosynthesis of terpenoids due to recent advances in synthetic biology and metabolic engineering. New techniques for gene discovery have expanded our search space for novel terpene synthesis pathways and unlocked unrealized potential for the microbial production of more complex derivatives. Additionally, numerous advances in host and pathway engineering have allowed for the production of terpenoids requiring oxidation and glycosylation, effectively expanding the potential target space. These advances will lay the foundation for the microbial biosynthesis of a seemingly infinite domain of terpenoids with varying applications. [ABSTRACT FROM AUTHOR]

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses. [ABSTRACT FROM AUTHOR]

Reciprocal best match graphs (RBMGs) are vertex colored graphs whose vertices represent genes and the colors the species where the genes reside. Edges identify pairs of genes that are most closely related with respect to an underlying evolutionary tree. In practical applications this tree is unknown and the edges of the RBMGs are inferred by quantifying sequence similarity. Due to noise in the data, these empirically determined graphs in general violate the condition of being a "biologically feasible" RBMG. Therefore, it is of practical interest in computational biology to correct the initial estimate. Here we consider deletion (remove at most k edges) and editing (add or delete at most k edges) problems. We show that the decision version of the deletion and editing problem to obtain RBMGs from vertex colored graphs is NP-hard. Using known results for the so-called bicluster editing, we show that the RBMG editing problem for 2-colored graphs is fixed-parameter tractable. A restricted class of RBMGs appears in the context of orthology detection. These are cographs with a specific type of vertex coloring known as hierarchical coloring. We show that the decision problem of modifying a vertex-colored graph (either by edge-deletion or editing) into an RBMG with cograph structure or, equivalently, to an hierarchically colored cograph is NP-complete. [ABSTRACT FROM AUTHOR]

Computational biology is mainly concerned with discovering an object from a given set of observations that are supposed to be good approximations of the real object. Two important steps here are to define a way to measure the distance between different objects and to calculate the distance between two given objects. The main problem is then to find an object that has the minimum total distance to the given observations. We study two NP-hard problems formulated in computational biology. The minimum tree cut/paste distance problem asks for the minimum number of cut/paste operations we need to transform a tree to another tree. The minimum common integer partition problem asks for a minimum-cardinality integer partition of a number that refines two given integer partitions of the same number. We give parameterized algorithms for both problems. • A reduction from the minimum common string partition problem to the minimum tree cut/paste distance problem. • The first combined parameter algorithm for the minimum tree cut/paste distance problem on rooted and unlabeled trees. • The first single parameter algorithm for the minimum common string partition problem. [ABSTRACT FROM AUTHOR]

We introduce an extension of decision problems called resiliency problems. In a resiliency problem, the goal is to decide whether an instance remains positive after any (appropriately defined) perturbation has been applied to it. To tackle these kinds of problems, some of which might be of practical interest, we introduce a notion of resiliency for Integer Linear Programs (ILP) and show how to use a result of Eisenbrand and Shmonin (Math. Oper. Res., 2008) on Parametric Linear Programming to prove that ILP Resiliency is fixed-parameter tractable (FPT) under a certain parameterization. To demonstrate the utility of our result, we consider natural resiliency variants of several concrete problems, and prove that they are FPT under natural parameterizations. Our first results concern a four-variate problem which generalizes the Disjoint Set Cover problem and which is of interest in access control. We obtain a complete parameterized complexity classification for every possible combination of the parameters. Then, we introduce and study a resiliency variant of the Closest String problem, for which we extend an FPT result of Gramm et al. (Algorithmica, 2003). We also consider problems in the fields of scheduling and social choice. We believe that many other problems can be tackled by our framework. [ABSTRACT FROM AUTHOR]

Epigenomics is the study of modifications on the genetic material of a cell that do not depend on changes in the DNA sequence, since those latter involve specific proteins around which DNA wraps. The end result is that Epigenomic changes have a fundamental role in the proper working of each cell in Eukaryotic organisms. A particularly important part of Epigenomics concentrates on the study of chromatin, that is, a fiber composed of a DNA-protein complex and very characterizing of Eukaryotes. Understanding how chromatin is assembled and how it changes is fundamental for Biology. In more than thirty years of research in this area, Mathematics and Theoretical Computer Science have gained a prominent role, in terms of modeling and mining, regarding in particular the so-called 10 nm fiber. Starting from some very basic notions of Biology, we briefly illustrate the recent advances obtained via laboratory experiments on the organization and dynamics of chromatin. Then, we mainly concentrate our attention on the contributions given by Combinatorial and Informational Methodologies, that are at the hearth of Theoretical Computer Science, to the understanding of mechanisms determining the 10 nm fiber. We conclude highlighting several directions of investigation that are perceived as important and where Theoretical Computer Science can provide high impact results. [ABSTRACT FROM AUTHOR]

A diverse library of producers is characterized for production variants. Suitable selection systems allow growth only above the threshold, reducing the original library to the top producers. • Selections offer highest throughput for interrogation of biological diversity and optimization of cell factories. • Traditional selection systems are complemented by computational and synthetic biology. • Synthetic selection systems are subject to escape via detrimental mutations. • Redundant selection systems or positive and negative selections can reduce escape rates dramatically. Microbial cell factories offer new and sustainable production routes for high-value chemicals. However, identification of high producers within a library of clones remains a challenge. When product formation is coupled to growth, millions of metabolic variants can be effectively interrogated by growth selection, dramatically increasing the throughput of strain evaluation. While growth-coupled selections for cell factories have a long history of success based on metabolite auxotrophies and toxic antimetabolites, such methods are generally restricted to molecules native to their host metabolism. New synthetic biology tools offer the opportunity to rewire cellular metabolism to depend on specific and non-native products for growth. [ABSTRACT FROM AUTHOR]

Highlights • New biomedical technologies generate measurements at scale and in multiple dimensions. • Large and diverse biomedical data present fundamentally new challenges for machine learning. • Integrative approaches combine different types of data to provide a comprehensive systems view. • Data integration creates a holistic picture of the cell, human body, and disease. • Advances in machine learning bring exciting future for biomedical data integration. Abstract New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field. [ABSTRACT FROM AUTHOR]

Given a set of n strings of length L and a radius d , the closest string problem (CSP for short) asks for a string t s o l that is within a Hamming distance of d to each of the given strings. It is known that the problem is NP-hard and its optimization version admits a polynomial time approximation scheme (PTAS). A number of parameterized algorithms have been then developed to solve the problem when d is small. Among them, the relatively new ones have not been implemented before and their performance in practice was unknown. In this study, we implement all of them by careful engineering. For those that have been implemented before, our implementation is much faster. For some of those that have not been implemented before, our experimental results show that there exist huge gaps between their theoretical and practical performances. We also design a new parameterized algorithm for the binary case of CSP. The algorithm is deterministic and runs in O (n 2 L + n 2 d ⋅ 6.16 d) time, while the previously best deterministic algorithm runs in O (n L + n d 3 ⋅ 6.731 d) time. [ABSTRACT FROM AUTHOR]

• Technologies such as single-cell RNA sequencing have rapidly progressed in recent years. • Various methods exist for preparing libraries for sequencing, each with its own limitations and advantages. • There are several analytical approaches for analyzing the large amount of data generated by single-cell sequencing experiments. • Single-cell RNA sequencing studies can reveal how distinct cell populations contribute to brain organization and and disease-relevant behaviors. Single-cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology, and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data. [ABSTRACT FROM AUTHOR]

• The same disease and an individual's responses to drugs can vary considerably. • The central reason for these differences is the complexity of the human body. • The differences in responses pose severe challenges for personalized medicine. • Computational systems biology is a powerful tool for addressing these challenges. • Different modeling modalities can support and aid rational personalized medicine. The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution. [ABSTRACT FROM AUTHOR]

Chemoresistance is a primary cause of breast cancer treatment failure, and protein-protein interactions significantly contribute to chemoresistance during different stages of breast cancer progression. In pursuit of novel biomarkers and relevant protein-protein interactions occurring during the emergence of breast cancer chemoresistance, we used a computational predictive biological (CPB) approach. CPB identified associations of adhesion molecules with proteins connected with different breast cancer proteins associated with chemoresistance. This approach identified an association of Integrin β1 (ITGB1) with chemoresistance and breast cancer stem cell markers. ITGB1 activated the Focal Adhesion Kinase (FAK) pathway promoting invasion, migration, and chemoresistance in breast cancer by upregulating Erk phosphorylation. FAK also activated Wnt/Sox2 signaling, which enhanced self-renewal in breast cancer. Activation of the FAK pathway by ITGB1 represents a novel mechanism linked to breast cancer chemoresistance, which may lead to novel therapies capable of blocking breast cancer progression by intervening in ITGB1-regulated signaling pathways. [Display omitted] • Developed a novel Computational-Predictive-Biological (CPB) strategy to predict different types of biomarkers in cancers. • CBP identified that ITGB1 is associated with chemoresistance, and cancer stem cell associated proteins in breast cancer. • ITGB1 regulated chemoresistance via activation of FAK pathway which regulates survival pathways, invasion, migration. • ITGB1 also regulated activated Wnt/Sox2 signaling through FAK, which enhanced self-renewal in breast cancer. • Thus, CBP established a unified pathway to regulate chemoresistance in breast cancer and ITGB1 as a potential biomarker. [ABSTRACT FROM AUTHOR]

Abstract Biological production and oxidation of hydrogen is mediated by hydrogenases, key enzymes for these energy-relevant reactions. Synthesis of [NiFe] hydrogenases involves a complex series of biochemical reactions to assemble protein subunits and metallic cofactors required for enzyme function. A final step in this biosynthetic pathway is the processing of a C-terminal tail (CTT) from its large subunit, thus allowing proper insertion of nickel in the unique NiFe(CN) 2 CO cofactor present in these enzymes. In silico modelling and Molecular Dynamics (MD) analyses of processed vs. unprocessed forms of Rhizobium leguminosarum bv. viciae (Rlv) hydrogenase large subunit HupL showed that its CTT (residues 582-596) is an intrinsically disordered region (IDR) that likely provides the required flexibility to the protein for the final steps of proteolytic maturation. Prediction of pKa values of ionizable side chains in both forms of the enzyme's large subunit also revealed that the presence of the CTT strongly modify the protonation state of some key residues around the active site. Furthermore, MD simulations and mutant analyses revealed that two glutamate residues (E27 in the N-terminal region and E589 inside the CTT) likely contribute to the process of nickel incorporation into the enzyme. Computational analysis also revealed structural details on the interaction of Rlv hydrogenase LSU with the endoprotease HupD responsible for the removal of CTT. Highlights • C-terminal tails of NiFe hydrogenases are Intrinsically Disordered Region (IDRs). • IDRs might provide flexibility for interaction with protease and other accessory proteins. • Modelling of [NiFe] hydrogenase unprocessed form reveals changes in the environment of key residues following processing. • Molecular dynamics and mutant analyses indicate that residues E27 and E589 are relevant for nickel incorporation. [ABSTRACT FROM AUTHOR]

Abstract A reduced-complexity numerical model, LX-Shore, is developed to simulate shoreline evolution along wave-dominated sandy coasts. The model can handle any sandy shoreline geometries (e.g. sand spits, islands), including non-erodible areas such as coastal defenses and headlands, and is coupled with a spectral wave model to cope with complex nearshore wave fields. Shoreline change is primarily driven by the gradients in total longshore sediment transport and by the cross-shore transport owing to variability in incident wave energy. Application to academic cases and a real coast highlights the potential of LX-Shore to simulate shoreline change on timescales from hours (storm) to decades with low computational cost. LX-Shore opens new perspectives in terms of knowledge on the primary mechanisms locally driving shoreline change and for ensemble-based simulations of future shoreline evolution. Highlights • A new shoreline change model is developed for wave-dominated sandy coasts: LX-Shore. • The numerical implementation allows addressing complex shoreline plan-view shapes. • LX-Shore couples cross-shore and alongshore processes. • LX-Shore simulates shoreline variability up to decades with low computational cost. • LX-Shore can provide new insights into the mechanisms controlling shoreline change. [ABSTRACT FROM AUTHOR]

Abstract Gram-negative multidrug resistance currently presents a serious threat to public health with infections effectively rendered untreatable. Multiple molecular mechanisms exist that cause antibiotic resistance and in addition, the last three decades have seen slowing rates of new drug development. In this review, we summarize the use of various computational techniques for investigating the mechanisms of multidrug resistance mediated by Gram-negative tripartite efflux pumps and membranes. Recent work in our lab combines data-driven sequence and structure analyses to study the interactions and dynamics of these bacterial components. Computational studies can complement experimental methodologies for gaining crucial insights into combatting multidrug resistance. [ABSTRACT FROM AUTHOR]

The interactions of amyloid peptides with cell membranes play an important role in maintaining the integrity and functionality of cell membrane. A thorough molecular-level understanding of the structure, dynamics, and interactions between amyloid peptides and cell membranes is critical to amyloid aggregation and toxicity mechanisms for the bench-to-bedside applications. Here we review the most recent computational studies of amyloid peptides at model cell membranes. Different mechanisms of action of amyloid peptides on/in cell membranes, targeted by different computational techniques at different lengthscales and timescales, are rationally discussed. Finally, we have proposed some new insights into the remaining challenges and perspectives for future studies to improve our understanding of the activity of amyloid peptides associated with protein-misfolding diseases. This article is part of a Special Issue entitled: Protein Aggregation and Misfolding at the Cell Membrane Interface edited by Ayyalusamy Ramamoorthy. [ABSTRACT FROM AUTHOR]

Amyloidogenic proteins are related to a variety of amyloid diseases, such as type 2 diabetes (T2D), Alzheimer's disease (AD) and Parkinson's disease (PD). The amyloid proteins in which this review focuses include amylin, Aβ, tau and α-synuclein. Understanding the molecular mechanisms in which these amyloidogenic proteins interact with membranes is a challenging research to both experimental and computational studies. This review illustrates recent studies on amyloid-membrane interactions, but it mainly focuses on the challenge issues related to experimental techniques to investigate at the molecular level these interactions and provides thoughts and outlook for future computational studies. [ABSTRACT FROM AUTHOR]

The quest for colorful components (connected components where each color is associated with at most one vertex) inside a vertex-colored graph has been widely considered in the last ten years. Here we consider two variants, Minimum Colorful Components (MCC) and Maximum Edges in transitive Closure (MEC), introduced in 2011 in the context of orthology gene identification in bioinformatics. The input of both MCC and MEC is a vertex-colored graph. MCC asks for the removal of a subset of edges, so that the resulting graph is partitioned in the minimum number of colorful connected components; MEC asks for the removal of a subset of edges, so that the resulting graph is partitioned in colorful connected components and the number of edges in the transitive closure of such a graph is maximized. We study the parameterized and approximation complexity of MCC and MEC, for general and restricted instances. For MCC on trees we show that the problem is basically equivalent to Minimum Cut on Trees, thus MCC is not approximable within factor 1.36 − ε , it is fixed-parameter tractable and it admits a poly-kernel (when the parameter is the number of colorful components). Moreover, we show that MCC, while it is polynomial time solvable on paths, it is NP-hard even for graphs with constant distance to disjoint paths number. Then we consider the parameterized complexity of MEC when parameterized by the number k of edges in the transitive closure of a solution (the graph obtained by removing edges so that it is partitioned in colorful connected components). We give a fixed-parameter algorithm for MEC parameterized by k and, when the input graph is a tree, we give a poly-kernel. [ABSTRACT FROM AUTHOR]

A new numerical method for the waves-damage interaction and the post-failure response analysis of laminated composite beams under an impact load is presented in this paper. The studied structure is made of E-glass/epoxy and it is decomposed in undamaged and damaged substructures. The material properties in the damaged substructure are modified according to sudden material property degradation rule. The damage describes the creation of micro-discontinuities in the structure during the loading. The enhanced formulation considered in this paper can be used to target this problem. The numerical method combines the Finite Element (FE) method to model the discontinuous regions, impact region and damaged region, and the wave finite element (WFE) to accurately model the periodic substructures (intact substructures). This hybrid method enables the calculation of the waves-damage interaction scattering coefficients and the response of the whole structure in both the frequency as well as in the time domain and it provides the reduction of the computational cost of analyses in space and time. Several numerical simulations have been performed, first to validate the accuracy of the results predicted by the proposed formulation and second to investigate the damage effect on the dynamic behaviour of the structure. [ABSTRACT FROM AUTHOR]

Current research in the field of computational biology often involves simulations on high-performance computer clusters. It is crucial that the code of such simulations is efficient and correctly reflects the model specifications. In this paper, we present an optimization strategy for agent-based simulations of biological dynamics using Intel Xeon Phi coprocessors, demonstrated by a prize-winning entry of the “Intel Modern Code Developer Challenge” competition. These optimizations allow simulating various biological mechanisms, in particular the simulation of millions of cells, their proliferation, movements and interactions in 3D space. Overall, our results demonstrate a powerful approach to implement and conduct very detailed and large-scale computational simulations for biological research. We also highlight the main difficulties faced when developing such optimizations, in particular the assessment of the simulation accuracy, the dependencies between different optimization techniques and counter-intuitive effects in the speed of the optimized solution. The overall speedup of 595 ×  shows a good parallel scalability. [ABSTRACT FROM AUTHOR]

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras / Trp53 -mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses. [Display omitted] • Interacting networks of lymphocytes (lymphonets) from in the KP GEMM of lung cancer • Small lymphonets have mostly T cells, and B cell fraction rises as networks enlarge • A key feature of lymphonets is that they contain TCF1+PD-1+CD8+ T cell progenitors • Lymphonets gain cytotoxic CD8+ T cells after immunotherapy Gaglia et al. find striking changes in the spatial arrangement of immune cells in response to tumor antigens. T and B cells are recruited in lymphocyte networks ("lymphonets"), which contain progenitor T cells. After immunotherapy, lymphonets gain cytotoxic T cells, likely due to progenitor cell differentiation and activation in this distinct immune environment. [ABSTRACT FROM AUTHOR]

Emerging and reemerging viral infections represent a major concern for human public health emergencies and there is an urgent need for the development of broad-spectrum antivirals and next generation vaccines. By using integrated system-wide approaches including genome-wide gene silencing profiling, miRNA profiling and proteomics via high-throughput techniques, combined with bio-imaging and computational biology, we attempt to understand the biological complexity of virus-host interactions and translating it into antiviral strategies against these viral pathogens. To address the urgent need for treatment options, we have evaluated an array of antiviral strategies from high throughput screening of small molecule compound libraries for potential antivirals against these viruses, to the utilization of molecular intervention as well as the development of DNA launched replicon based vaccines. In vivo efficacy of these antiviral approaches are also illustrated with murine models established for these viral infections. In this talk, I will illustrate with the human enteroviruses (EV-A71) that have important medical implications on the pediatric populations in Asia. Together, these studies have provided the basis for the development of effective antiviral approaches that can be clinically validated as viable antiviral strategies against these medically important viral pathogens. [ABSTRACT FROM AUTHOR]

In this article we study the treewidth of the display graph , an auxiliary graph structure obtained from the fusion of phylogenetic (i.e., evolutionary) trees at their leaves. Earlier work has shown that the treewidth of the display graph is bounded if the trees are in some formal sense topologically similar. Here we further expand upon this relationship. We analyze a number of reduction rules, commonly used in the phylogenetics literature to obtain fixed parameter tractable algorithms. In some cases (the subtree reduction) the reduction rules behave similarly with respect to treewidth, while others (the cluster reduction) behave very differently, and the behavior of the chain reduction is particularly intriguing because of its link with graph separators and forbidden minors. We also show that the gap between treewidth and Tree Bisection and Reconnect (TBR) distance can be infinitely large, and that unlike, for example, planar graphs the treewidth of the display graph can be as much as linear in its number of vertices. A number of other auxiliary results are given. We conclude with a discussion and list a number of open problems. [ABSTRACT FROM AUTHOR]

In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), i.e. a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity. [ABSTRACT FROM AUTHOR]

This paper presents a largely improved version of the VF2 algorithm for the Subgraph Isomorphism Problem . The improvements are twofold. Firstly, it is based on a new approach for determining the matching order of the nodes, and secondly, more efficient — nevertheless easier to compute — cutting rules are proposed. They together reduce the search space significantly. In addition to the usual Subgraph Isomorphism Problem , the paper also presents specialized algorithms for the Induced Subgraph Isomorphism and for the Graph Isomorphism Problems . Finally, an extensive experimental evaluation is provided using a wide range of inputs, including both real-life biological and chemical datasets and standard randomly generated graph series. The results show major and consistent running time improvements over the other known methods. The C++ implementations of the algorithms are available open-source as part of the LEMON graph and network optimization library. [ABSTRACT FROM AUTHOR]

Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers. [ABSTRACT FROM AUTHOR]

Global behavior for stochastic system of regulation of bacterial tryptophan ( trp ) operon is analyzed in this paper. This bioprocess cannot avoid the internal and external disturbances reflected on the cell growth rate within the biosynthesis of trp . These factors can affect the integrated results of repression and attenuation, leading to genetic circuit or switch. The equilibria and bifurcations of the deterministic system are analyzed. Then, a stochastic model is presented by considering the effect of a bounded Markov diffusion process on critical parameters. In order to analyze the global behavior, we compute the control sets through solving the associated control system by using parallel control set algorithm. In particular, we compute the bifurcation as discontinuous topology changes in the support of a stationary measure. The probability distributions of relative supports are also provided. The results provide indication on how the disturbances affect the bioprocess with sustained oscillations. [ABSTRACT FROM AUTHOR]

The large-scale generation of ‘-omic’ data holds the potential to increase and deepen our understanding of biological phenomena, but the ability to synthesize information and extract knowledge from these data sets still represents a significant challenge. Bottom-up systems biology overcomes this hurdle through the integration of disparate -omic data types, and absolutely quantified experimental measurements allow for direct integration into quantitative, mechanistic models. The human red blood cell has served as a starting point for the application of systems biology approaches and has been the focus of a recent burst of generated quantitative metabolomics and proteomics data. Thus, the red blood cell represents the perfect case study through which to examine our ability to glean knowledge from the integration of multiple disparate data types. [ABSTRACT FROM AUTHOR]

HilD is an AraC-like transcriptional regulator that plays a central role in Salmonella virulence. HilD controls the expression of the genes within the Salmonella pathogenicity island 1 (SPI-1) and of several genes located outside SPI-1, which are mainly required for Salmonella invasion of host cells. The expression, amount, and activity of HilD are tightly controlled by the activities of several factors. The HilE protein represses the expression of the SPI-1 genes through its interaction with HilD; however, the mechanism by which HilE affects HilD is unknown. In this study, we used genetic and biochemical assays revealing how HilE controls the transcriptional activity of HilD. Wefound that HilD needs to assemble in homodimers to induce expression of its target genes. Our results further indicated that HilE individually interacts with each the central and the C-terminal HilD regions, mediating dimerization and DNA binding, respectively. We also observed that these interactions consistently inhibit HilD dimerization and DNA binding. Interestingly, a computational analysis revealed that HilE shares sequence and structural similarities with Hcp proteins, which act as structural components of type 6 secretion systems in Gram-negative bacteria. In conclusion, our results uncover the molecular mechanism by which the Hcp-like protein HilE controls dimerization andDNAbinding of the virulence-promoting transcriptional regulator HilD. Our findings may indicate that HilE's activity represents a functional adaptation during the evolution of Salmonella pathogenicity. [ABSTRACT FROM AUTHOR]

A four-point single-edge-notch-beam (SENB) test specimen loaded in displacement control (fixed grip) is proposed for studying crack deflection at bi-material interfaces. In order to ensure stable crack growth, a novel analytical model of the four-point SENB specimen in fixed grip is derived and compared with numerical models. Model results show that the specimen should be short and thick, and the start-crack length should be deep for the crack to propagate stable towards the bi-material interface. Observations from experimental tests of four-point SENB specimens with different start-crack lengths, confirmed that the crack grows stable if the start-crack length is deep and unstable if not. [ABSTRACT FROM AUTHOR]

We present a computational framework for conceptual blending , a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles , described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music. [ABSTRACT FROM AUTHOR]

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies. [ABSTRACT FROM AUTHOR]

An overarching goal of environmental genomics is to leverage sensitive suites of markers that are robust and reliable to assess biological responses in a range of species inhabiting variable environments. The objective of this study was to identify core groups of transcripts and molecular signaling pathways that respond to 17alpha-ethylinestadiol (EE2), a ubiquitous estrogenic contaminant, using transcriptome datasets generated from six independent laboratories. We sought to determine which biomarkers and gene networks were those most robust and reliably detected in multiple laboratories. Six laboratories conducted microarray analysis in pieces of the same liver from male fathead minnows exposed to ∼15 ng/L EE2 for 96 h. There were common transcriptional networks identified in every dataset. These included down-regulation of gene networks associated with blood clotting, complement activation, triglyceride storage, and xenobiotic metabolism. Noteworthy was that more than ∼85% of the gene networks were suppressed by EE2. Leveraging both these data and those mined from the Comparative Toxicogenomics Database (CTD), we narrowed in on an EE2-responsive transcriptional network. All transcripts in this network responded ∼ ± 5-fold or more to EE2, increasing reliability of detection. This network included estrogen receptor alpha, transferrin, myeloid cell leukemia 1, insulin like growth factor 1, insulin like growth factor binding protein 2, and methionine adenosyltransferase 2A. This estrogen-responsive interactome has the advantage over single markers (e.g. vitellogenin) in that these entities are directly connected to each other based upon evidence of expression regulation and protein binding. Thus, it represents an interacting functional suite of estrogenic markers. Vitellogenin, the gold standard for estrogenic exposures, can show high individual variability in its response to estrogens, and the use of a multi-gene approach for estrogenic chemicals is expected to improve sensitivity. In our case, the coefficient of variation was significantly lowered by the gene network (∼67%) compared to Vtg alone, supporting the use of this transcriptional network as a sensitive alternative for detecting estrogenic effluents and chemicals. We propose that screening chemicals for estrogenicity using interacting genes within a defined expression network will improve sensitivity, accuracy, and reduce the number of animals required for endocrine disruption assessments. [ABSTRACT FROM AUTHOR]

The Graph Motif problem was introduced in 2006 in the context of biological networks. It consists of deciding whether or not a multiset of colors occurs in a connected subgraph of a vertex-colored graph. Graph Motif has been mostly analyzed from the standpoint of parameterized complexity. The main parameters which came into consideration were the size of the multiset and the number of colors. In the many utilizations of Graph Motif , however, the input graph originates from real-life applications and has structure. Motivated by this prosaic observation, we systematically study its complexity relatively to graph structural parameters. For a wide range of parameters, we give new or improved FPT algorithms, or show that the problem remains intractable. For the FPT cases, we also give some kernelization lower bounds as well as some ETH-based lower bounds on the worst case running time. Interestingly, we establish that Graph Motif is W[1] -hard (while in W[P] ) for parameter max leaf number, which is, to the best of our knowledge, the first problem to behave this way. [ABSTRACT FROM AUTHOR]

Computational proteomics is a constantly growing field to support end users with powerful and reliable tools for performing several computational steps within an analytics workflow for proteomics experiments. Typically, after capturing with a mass spectrometer, the proteins have to be identified and quantified. After certain follow-up analyses, an optional targeted approach is suitable for validating the results. The de.NBI (German network for bioinformatics infrastructure) service center in Dortmund provides several software applications and platforms as services to meet these demands. In this work, we present our tools and services, which is the combination of SearchGUI and PeptideShaker. SearchGUI is a managing tool for several search engines to find peptide spectra matches for one or more complex MS 2 measurements. PeptideShaker combines all matches and creates a consensus list of identified proteins providing statistical confidence measures. In a next step, we are planning to release a web service for protein identification containing both tools. This system will be designed for high scalability and distributed computing using solutions like the Docker container system among others. As an additional service, we offer a web service oriented database providing all necessary high-quality and high-resolution data for starting targeted proteomics analyses. The user can easily select proteins of interest, review the according spectra and download both protein sequences and spectral library. All systems are designed to be intuitively and user-friendly operable. [ABSTRACT FROM AUTHOR]

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer , a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design. [ABSTRACT FROM AUTHOR]

The analysis of high-throughput mass spectrometry-based proteomics data must address the specific challenges of this technology. To this end, the comprehensive proteomics workflow offered by the de.NBI service center BioInfra.Prot provides indispensable components for the computational and statistical analysis of this kind of data. These components include tools and methods for spectrum identification and protein inference , protein quantification , expression analysis as well as data standardization and data publication. All particular methods of the workflow which address these tasks are state-of-the-art or cutting edge. As has been shown in previous publications, each of these methods is adequate to solve its specific task and gives competitive results. However, the methods included in the workflow are continuously reviewed, updated and improved to adapt to new scientific developments. All of these particular components and methods are available as stand-alone BioInfra.Prot services or as a complete workflow. Since BioInfra.Prot provides manifold fast communication channels to get access to all components of the workflow (e.g., via the BioInfra.Prot ticket system: bioinfraprot@rub.de) users can easily benefit from this service and get support by experts. [ABSTRACT FROM AUTHOR]