Computational systems chemical biology

Abstract

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Fig. 1

Contribution of Cheminformatics to Systems Biology. It is expected that computational modeling will afford the prediction of chemical structures active against individual (or multiple) targets while PBPK approaches will afford the estimates of compound distribution and accumulation in target tissues. Yet the knowledge of pathways will enable to predict the effect of chemicals on the entire system in the context of steering the disease-affected network towards a normal state

Fig. 2

Data curation workflow.

Fig. 3

Glyoxylate pathway (schematic)

Fig. 4

Property distribution for BDDCS classes 0-4 for ClogP (left) and PSA (right).

Fig. 5

Flowchart of predictive QSAR modeling workflow implementing combinatorial QSAR modeling and extensive model validation procedures.

Fig. 6

BioXyce simulation of tryptophan biosynthesis; comparison to experimental data.

Fig. 7

The Malate Synthase Cavities.

Fig. 8

Glyoxylate and Malate in the presence and absence of inhibitory molecules

Fig. 9

BioXyce workflow: Information from various data sources is integrated and transferred to Xyce input for biological network simulation. The Mtb glyoxylate pathway is depicted