Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse engineering procedure, thus biasing the search towards biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. To allow direct comparison with other methods, we use a benchmark dataset from an in vivo synthetic-biology gene network, which has been published as a reverse engineering challenge for the second DREAM conference

Combining Multiple Results of a Reverse Engineering Algorithm: Application to the DREAM Five Gene Network Challenge

The output of reverse engineering methods for biological networks is often not a single network prediction, but an ensemble of networks that are consistent with the experimentally measured data. In this paper, we consider the problem of combining the information contained within such an ensemble in order to (1) make more accurate network predictions and (2) estimate the reliability of these predictions. We review existing methods, discuss their limitations, and point out possible research directions towards more advanced methods for this purpose. The potential of considering ensembles of networks, rather than individual inferred networks, is demonstrated by showing how an ensemble voting method achieved winning performance on the Five Gene Network Challenge of the second DREAM conference (Dialogue on Reverse Engineering Assessment and Methods 2007, New York, NY)

Evolutionary reverse engineering of gene networks

The expression of genes is controlled by regulatory networks, which perform fundamental information processing and control mechanisms in a cell. Unraveling and modelling these networks will be indispensable to gain a systems-level understanding of biological organisms and genetically related diseases. In this thesis, we present an evolutionary reverse engineering method, which allows to simultaneously infer both the wirings and nonlinear dynamical models of gene regulatory networks from gene expression data. The proposed method reconstructs gene networks by mimicking the natural evolutionary process that constructed them. This is achieved by modelling both the way in which gene networks are encoded in the biological genome, and the different types of mutations and recombinations that drive their evolution, using an artificial genome called Analog Genetic Encoding (AGE). Since AGE mimics the evolutionary forces and constraints that shape biological gene networks, the reconstruction is naturally guided towards biologically plausible solutions. Consequently, the search space is explored more efficiently, and the networks are recovered more reliably, than with alternative methods. We have confirmed the state-of-the-art performance of AGE both in vivo (on real gene networks) and in silico (on simulated networks). In particular, AGE achieved winning performance in the in vivo gene network inference inference challenge of the 2nd DREAM (Dialogue on Reverse Engineering Assessment and Methods) conference, which consisted in predicting the structure of a synthetic-biology gene network in Saccharomyces cerevisiae from time-series data. In vivo performance assessment of network-inference methods is problematic because it is in general not possible to systematically validate predictions, except for few well-characterized gene networks. Consequently, in silico benchmarks are essential to understand the performance of network-inference methods. We have developed tools to generate biologically plausible in silico gene networks, which allow realistic performance assessment of network-inference methods. In contrast to previous in silico benchmarks, we generate network structures by extracting modules from known gene networks of model organisms, instead of using random graphs. Furthermore, we simulate network dynamics using more realistic kinetic models, which include both mRNA and proteins. We have implemented this framework in an open-source Java tool called GeneNetWeaver (GNW). Using GNW we have generated benchmarks for community-wide challenges of the 3rd and 4th DREAM conference (the DREAM in silico network challenges). Here, we assess the performance of 29 network-inference methods, which have been applied independently by participating teams of the DREAM3 challenge. Performance profiling on individual network motifs reveals that current inference methods are affected, to various degrees, by three types of systematic prediction errors. We find that these errors are induced by inaccurate prior assumptions of prevalent gene-network models. The evolutionary reverse engineering approach, which would have ranked 3rd in this challenge, can be used with a wide range of nonlinear models. It could thus provide the necessary framework for the development of models that better approximate different types of gene regulation, thereby enabling ever more accurate reconstruction of gene networks

Reconstruction And Analysis Of The Molecular Programs Involved In Deciding Mammalian Cell Fate

Cellular function hinges on the ability to process information from the outside environment into specific decisions. Ultimately these processes decide cell fate, whether it be to undergo proliferation, apoptosis, differentiation, migration and other cellular functions. These processes can be thought of as finely tuned programs evolved to maintain robust function in spite of environmental perturbations. Malfunctions in these programs can lead to improper cellular function and various disease states. To develop more effective, personalized and even preventative therapeutics we must attain a better, more detailed, understanding of the programs involved. To this end we have employed mechanistic mathematical modeling to a variety of complex cellular programs. In Chapter 1, we review a variety of computational methods have have been used successfully in different areas of biotechnology. In Chapter 2, we present the software platform UNIVERSAL, which was developed in our lab. UNIVERSAL is an extensible code generation framework for Mac OS X which produces editable, fully commented platform-independent physiochemical model code in several common programming languages from a variety of inputs. UNIVERSAL generates mass-action ODE models of intracellular signal transduction processes and model analysis code, such as adjoint sensitivity balances. We employed the mass-action ODE framework, as generated by UNIVERSAL, commonly throughout the studies presented here. In Chapter 3, we introduce a variety of modeling strategies in the context of EGF-induced Eukaryotic transcription. We demon- strated the ability to make meaningful and statistically consistent model predictions despite considerable parametric uncertainty. In Chapter 4, we constructed a mathematical model to study a mechanism for androgen independent proliferation in prostate cancer. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. Translation became progressively more important in androgen independent cells. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen independent prostate cancers. In Chapter 5, A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell lines with and without RA. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. We investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. In Chapter 6, we carried out experimental studies to reduce the structural uncertainty of the HL60 model. Result from the HL-60 model cRaf as the most critical component of the MAPK cascade. To investigate the role of cRaf in RA-induced differentiation we observed the effect of cRaf kinase inhibition. Furthermore, we interrogated a panel of proteins to identify RA responsive cRaf binding partner. We found that cRaf kinase activity was necessary for functional ROS response, but not for RA-induced growth arrest. Based on our findings, we proposed a simplified ontrol architecture for sustained MAPK activation. Computational modeling identified a bistability suggesting that the MAPK activation was self-sustaining. This result was experimentally validated, and could explain previously observed cellular memory effects. Taken together, the results of these studies demonstrated that computational modeling can identify therapeutically relevant targets for human disease such as cancer. Furthermore, we demonstrated the ability of an iterative strategy between computational and experimental analysis to provide insight on key regulator circuits for complex programs involved in deciding cell fate