Descriptor-free molecular discovery in large libraries by adaptive substituent reordering

Molecular discovery often involves identification of the best functional groups (substituents) on a scaffold. When multiple substitution sites are present, the number of possible substituent combinations can be very large. This article introduces a strategy for efficiently optimizing the substituent combinations by iterative rounds of compound sampling, substituent reordering to produce the most regular property landscape, and property estimation over the landscape. Application of this approach to a large pharmaceutical compound library demonstrates its ability to find active compounds with a threefold reduction in synthetic and assaying effort, even without knowing the molecular identity of any compound.     

Phenol oxidase activation in Drosophila: A cascade of reactions

Our earlier evidence concerning the complexity of the activation process for Drosophila phenol oxidase has been confirmed by separation and purification of six proteins involved. This is a minimal number required in a reaction series or cascade to yield active enzyme, and at least two more proteins are known to be involved. A simpler system involving only the last step with one precursor and one activation as has been reported in the literature is consistent with the cascade picture, but the whole complex system must be considered when dealing with genetic and developmental regulation of pigmentation and sclerotization. Details are given for partial or complete purification of six of the proteins involved and evidence for their modes of interaction is presented.     

Identifying biological network structure, predicting network behavior, and classifying network state with High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state.     

Dissecting enzyme regulation by multiple allosteric effectors: nucleotide regulation of aspartate transcarbamoylase

The enzyme aspartate transcarbamoylase (ATCase, EC 2.1.3.2 of Escherichia coli), which catalyzes the committed step of pyrimidine biosynthesis, is allosterically regulated by all four ribonucleoside triphosphates (NTPs) in a nonlinear manner. Here, we dissect this regulation using the recently developed approach of random sampling-high-dimensional model representation (RS-HDMR). ATCase activity was measured in vitro at 300 random NTP concentration combinations, each involving (consistent with in vivo conditions) all four NTPs being present. These data were then used to derive a RS-HDMR model of ATCase activity over the full four-dimensional NTP space. The model accounted for 90% of the variance in the experimental data. Its main elements were positive ATCase regulation by ATP and negative by CTP, with the negative effects of CTP dominating the positive ones of ATP when both regulators were abundant (i.e., a negative cooperative effect of ATP x CTP). Strong sensitivity to both ATP and CTP concentrations occurred in their physiological concentration ranges. UTP had only a slight effect, and GTP had almost none. These findings support a predominant role of CTP and ATP in ATCase regulation. The general approach provides a new paradigm for dissecting multifactorial regulation of biological molecules and processes.     

Tissue levels of haloperidol by radioreceptor assay and behavioral effects of haloperidol in the rat

A radioreceptor assay verified by independent biochemical methods was used to evaluate tissue levels of neuroleptic activity in serum and brain extracts after injections of haloperidol in the rat. The assay detected activity between doses of 0.1 and 10 mg/kg at times between 0.25 and 12 hrs. Tissue levels in blood and brain were highly correlated and corresponded well with a behavioral test of catalepsy at one hour after drug administration. This relationship between brain levels and behavior persisted but changed quantitatively over time.     

The recessive phenotype of forked can be uncovered by heat shock in Drosophila

Heat shock uncovers the recessive forked phenotype when heterozygotes between f^36a and wild-type are heated during sensitive periods in pupal development. We call the phenocopy of a mutant in such a heterozygote a heterocopy. The heterocopy in f^36a/+ is virtually identical to the mutant phenotype; however, bristles on different parts of the body are affected during different sensitive periods. We discuss the hypothesis that the heat shock acts by affecting expression of the wild-type gene product corresponding to the mutant gene. The sensitive period for heterocopy induction in a specific tissue is proposed to correspond to the normal time of gene expression for the forked gene product in a particular tissue.     

Analysis of gene network robustness based on saturated fixed point attractors

The analysis of gene network robustness to noise and mutation is important for fundamental and practical reasons. Robustness refers to the stability of the equilibrium expression state of a gene network to variations of the initial expression state and network topology. Numerical simulation of these variations is commonly used for the assessment of robustness. Since there exists a great number of possible gene network topologies and initial states, even millions of simulations may be still too small to give reliable results. When the initial and equilibrium expression states are restricted to being saturated (i.e., their elements can only take values 1 or −1 corresponding to maximum activation and maximum repression of genes), an analytical gene network robustness assessment is possible. We present this analytical treatment based on determination of the saturated fixed point attractors for sigmoidal function models. The analysis can determine (a) for a given network, which and how many saturated equilibrium states exist and which and how many saturated initial states converge to each of these saturated equilibrium states and (b) for a given saturated equilibrium state or a given pair of saturated equilibrium and initial states, which and how many gene networks, referred to as viable, share this saturated equilibrium state or the pair of saturated equilibrium and initial states. We also show that the viable networks sharing a given saturated equilibrium state must follow certain patterns. These capabilities of the analytical treatment make it possible to properly define and accurately determine robustness to noise and mutation for gene networks. Previous network research conclusions drawn from performing millions of simulations follow directly from the results of our analytical treatment. Furthermore, the analytical results provide criteria for the identification of model validity and suggest modified models of gene network dynamics. The yeast cell-cycle network is used as an illustration of the practical application of this analytical treatment.