CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts.

Estimating the predictability of cancer evolution.

Motivation: How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo.

Syntrophy emerges spontaneously in complex metabolic systems.

Syntrophy allows a microbial community as a whole to survive in an environment, even though individual microbes cannot. The metabolic interdependence typical of syntrophy is thought to arise from the accumulation of degenerative mutations during the sustained co-evolution of initially self-sufficient organisms. An alternative and underexplored possibility is that syntrophy can emerge spontaneously in communities of organisms that did not co-evolve.

Genomic organization underlying deletional robustness in bacterial metabolic systems.

Large-scale DNA deletions and gene loss are pervasive in bacterial genomes. This observation raises the possibility that evolutionary adaptation has altered bacterial genome organization to increase its robustness to large-scale tandem gene deletions. To find out, we systematically analyzed 55 bacterial genome-scale metabolisms and showed that metabolic gene ordering renders an organism's viability in multiple nutrient environments significantly more robust against tandem multigene deletions than expected by chance.

Constraint and Contingency Pervade the Emergence of Novel Phenotypes in Complex Metabolic Systems.

An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. We know examples of such constraints, but we have no systematic understanding about their extent and causes for any one biological system. We here study metabolisms, genomically encoded complex networks of enzyme-catalyzed biochemical reactions, and the constraints they experience in bringing forth novel phenotypes that allow survival on novel carbon sources.

The potential for non-adaptive origins of evolutionary innovations in central carbon metabolism.

Background: Biological systems are rife with examples of pre-adaptations or exaptations. They range from the molecular scale - lens crystallins, which originated from metabolic enzymes - to the macroscopic scale, such as feathers used in flying, which originally served thermal insulation or waterproofing. An important class of exaptations are novel and useful traits with non-adaptive origins.

Phenotypic innovation through recombination in genome-scale metabolic networks.

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments.

Exhaustive Analysis of a Genotype Space Comprising 10(15 )Central Carbon Metabolisms Reveals an Organization Conducive to Metabolic Innovation.

All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients.

Historical contingency and the gradual evolution of metabolic properties in central carbon and genome-scale metabolisms.

Background: A metabolism can evolve through changes in its biochemical reactions that are caused by processes such as horizontal gene transfer and gene deletion. While such changes need to preserve an organism's viability in its environment, they can modify other important properties, such as a metabolism's maximal biomass synthesis rate and its robustness to genetic and environmental change. Whether such properties can be modulated in evolution depends on whether all or most viable metabolisms - those that can synthesize all essential biomass precursors - are connected in a space of all possible metabolisms.

PRKN, DJ-1, and PINK1 screening identifies novel splice site mutation in PRKN and two novel DJ-1 mutations.

We present results of mutation screening of PRKN gene in 93 Iranian Parkinson's disease (PD) patients with average age at onset (AAO) of 42.2 years. The gene was screened by direct sequencing and by a semi-quantitative PCR protocol for detection of sequence rearrangements.

PROSIGN: a method for protein secondary structure assignment based on three-dimensional coordinates of consecutive C(alpha) atoms.

The automatic assignment of secondary structure from three-dimensional atomic coordinates of proteins is an essential step for the analysis and modeling of protein structures. So different methods based on different criteria have been designed to perform this task. We introduce a new method for protein secondary structure assignment based solely on C(alpha) coordinates.