Prevailing drug discovery approaches focus on compounds with molecular selectivity, inhibiting disease-relevant targets over others in vitro. However in vivo, many such agents are not therapeutically selective, either because of undesirable activity at effective doses or because the biological system responds to compensate. In theory, drug combinations should permit increased control of such complex biology, but there is a common concern that therapeutic synergy will generally be mirrored by synergistic side-effects. Here we provide evidence, from 94,110 multi-dose combination experiments representing diverse disease areas and large scale flux balance simulations of inhibited bacterial metabolism, that multi-target synergies are more specific than single agent activities to particular cellular contexts. Using an anti-inflammatory combination, we show how multi-target synergy can achieve therapeutic selectivity in animals through differential target expression. Synergistic combinations can increase the number of selective therapies using the current pharmacopeia, and offer opportunities for more precise control of biological systems.

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.

Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.

Preservation of genetic information in DNA relies on shielding the nucleobases from damage within the double helix. Thermal fluctuations lead to infrequent events of the Watson-Crick basepair opening, or DNA "breathing", thus making normally buried groups available for modification and interaction with proteins. Fluctuational basepair opening implies the disruption of hydrogen bonds between the complementary bases and flipping of the base out of the helical stack. Prediction of sequence-dependent basepair opening probabilities in DNA is based on separation of the two major contributions to the stability of the double helix: lateral pairing between the complementary bases and stacking of the pairs along the helical axis. The partition function calculates the basepair opening probability at every position based on the loss of two stacking interactions and one base-pairing. Our model also includes a term accounting for the unfavorable positioning of the exposed base, which proceeds through a formation of a highly constrained small loop, or a ring. Quantitatively, the ring factor is found as an adjustable parameter from the comparison of the theoretical basepair opening probabilities and the experimental data on short DNA duplexes measured by NMR spectroscopy. We find that these thermodynamic parameters suggest nonobvious sequence dependent basepair opening probabilities.

Biological systems are robust, in that they can maintain stable phenotypes under varying conditions or attacks. Biological systems are also complex, being organized into many functional modules that communicate through interlocking pathways and feedback mechanisms. In these systems, robustness and complexity are linked because both qualities arise from the same underlying mechanisms. When perturbed by multiple attacks, such complex systems become fragile in both theoretical and experimental studies, and this fragility depends on the number of agents applied. We explore how this relationship can be used to study the functional robustness of a biological system using systematic high-order combination experiments. This presents a promising approach toward many biomedical and bioengineering challenges. For example, high-order experiments could determine the point of fragility for pathogenic bacteria and might help identify optimal treatments against multi-drug resistance. Such studies would also reinforce the growing appreciation that biological systems are best manipulated not by targeting a single protein, but by modulating the set of many nodes that can selectively control a system's functional state. Subject Categories: genomic and computational biology; metabolic and regulatory networks