John Arul Prakash Sekar

• Ph.D. Computational & Systems Biology
• Fellow at University of Pittsburgh

Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our limited formats for describing macromolecules. To overcome this barrier, we develop BpForms and BcForm...

Comprehensive simulations of the entire biochemistry of cells have great potential to help physicians treat disease and help engineers design biological machines. But such simulations must model networks of millions of molecular species and reactions. The Stochastic Simulation Algorithm (SSA) is widely used for simulating biochemistry, especially s...

A central challenge in science is to understand how behaviors emerge from complex networks. This often requires reusing and integrating heterogeneous information. Supplementary spreadsheets to journal articles are a key data source. Spreadsheets are popular because they are easy to read and write. However, spreadsheets are often difficult to reanal...

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as mo...

Graph operation nodes. Supported graph operation nodes for compact rule visualization. AddBond and DeleteBond are placed adjacent to the pair of components on which a bond is added or removed respectively. AddMol and DeleteMol are placed adjacent to the molecule that is added or removed respectively. AddBond/AddMol nodes have edges pointed outward...

Groups on the atom-rule graph. (A) On the full AR graph of Faeder et al. [9], the default heuristic groups phosphorylation sites on the same molecules (e.g., Rec_pY) and binding interactions between the same pairs of molecules (e.g., Lyn|Rec). Then, an algorithm groups rules that share the same edge signature, i.e., if they have the same edges to t...

Distribution of readability metrics for various visualization methods. Graph size and edge density of 27 rule-based models (blue) and their geometric mean (red) for 9 types of visualizations: (A) contact map, (B) conventional rule visualization, (C) compact rule visualization, (D) Simmune Network Viewer, (E) rule influence diagram, (F) full model a...

Supplementary material for readability analysis. (ZIP)

Comparison of AR graph and simmune network viewer. (A) A model in which three sites on a protein are activated in sequence. (B) The sequence is evident on the AR graph. (C) The sequence cannot be seen on the Simmune Network Viewer diagram because the three patterns used have the same molecule stoichiometry {A = 1} and are represented by the same no...

Supplementary material for tutorial. (ZIP)

Comparison of AR graph and rxncon regulatory graphs. (A) In BioNetGen, complex reaction mechanisms are specified as reaction rules and the AR graph is inferred by analyzing the specified rules. The reaction rule shown models trans-phosphorylation of receptor R in the ligand-crosslinked dimer configuration by recruited kinase K, a frequently encount...

Detailed methods, algorithms and rendering conventions. (DOCX)

Whole-cell dynamical models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed th...

Whole-cell models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed the existing...

Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous...

Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous...

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracel...

Rule-based modeling frameworks provide a specification format in which kinetic interactions are modeled as “reaction rules”. These rules are specified on phosphorylation motifs, domains, binding sites and other sub-molecular structures, and have proved useful for modeling signal transduction. Visual representations are necessary to understand indiv...

Rule-based modeling is a powerful way to model kinetic interactions in biochemical systems. Rules enable a precise encoding of biochemical interactions at the resolution of sites within molecules, but obtaining an integrated global view from sets of rules remains challenging. Current automated approaches to rule visualization fail to address the co...

BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation, and analysis of larger and more complex models...

Biological cells accomplish their physiological functions using interconnected networks of genes, proteins, and other biomolecules. Most interactions in biological signaling networks, such as bimolecular association or covalent modification, can be modeled in a physically realistic manner using elementary reaction kinetics. However, the size and co...