Abhishek Garg

Synchronous versus asynchronous modeling of gene regulatory networks

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process. Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html. Contact: abhishek.garg@epfl.ch

Synthetic biology in mammalian cells: next generation research tools and therapeutics

Recent progress in DNA manipulation and gene circuit engineering has greatly improved our ability to programme and probe mammalian cell behaviour. These advances have led to a new generation of synthetic biology research tools and potential therapeutic applications. Programmable DNA-binding domains and RNA regulators are leading to unprecedented control of gene expression and elucidation of gene function. Rebuilding complex biological circuits such as T cell receptor signalling in isolation from their natural context has deepened our understanding of network motifs and signalling pathways. Synthetic biology is also leading to innovative therapeutic interventions based on cell-based therapies, protein drugs, vaccines and gene therapies.

Engineering synthetic TAL effectors with orthogonal target sites

The ability to engineer biological circuits that process and respond to complex cellular signals has the potential to impact many areas of biology and medicine. Transcriptional activator-like effectors (TALEs) have emerged as an attractive component for engineering these circuits, as TALEs can be designed de novo to target a given DNA sequence. Currently, however, the use of TALEs is limited by degeneracy in the site-specific manner by which they recognize DNA. Here, we propose an algorithm to computationally address this problem. We apply our algorithm to design 180 TALEs targeting 20 bp cognate binding sites that are at least 3 nt mismatches away from all 20 bp sequences in putative 2 kb human promoter regions. We generated eight of these synthetic TALE activators and showed that each is able to activate transcription from a targeted reporter. Importantly, we show that these proteins do not activate synthetic reporters containing mismatches similar to those present in the genome nor a set of endogenous genes predicted to be the most likely targets in vivo. Finally, we generated and characterized TALE repressors comprised of our orthogonal DNA binding domains and further combined them with shRNAs to accomplish near complete repression of target gene expression.

Dynamic simulation of regulatory networks using SQUAD

BackgroundThe ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology.ResultsWe developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation.ConclusionThe simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

In vivo co-localization of enzymes on RNA scaffolds increases metabolic production in a geometrically dependent manner

Co-localization of biochemical processes plays a key role in the directional control of metabolic fluxes toward specific products in cells. Here, we employ in vivo scaffolds made of RNA that can bind engineered proteins fused to specific RNA binding domains. This allows proteins to be co-localized on RNA scaffolds inside living Escherichia coli. We assembled a library of eight aptamers and corresponding RNA binding domains fused to partial fragments of fluorescent proteins. New scaffold designs could co-localize split green fluorescent protein fragments to produce activity as measured by cell-based fluorescence. The scaffolds consisted of either single bivalent RNAs or RNAs designed to polymerize in one or two dimensions. The new scaffolds were used to increase metabolic output from a two-enzyme pentadecane production pathway that contains a fatty aldehyde intermediate, as well as three and four enzymes in the succinate production pathway. Pentadecane synthesis depended on the geometry of enzymes on the scaffold, as determined through systematic reorientation of the acyl-ACP reductase fusion by rotation via addition of base pairs to its cognate RNA aptamer. Together, these data suggest that intra-cellular scaffolding of enzymatic reactions may enhance the direct channeling of a variety of substrates.

An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments

With the increasing availability of experimental data on gene-gene and protein-protein interactions, modeling of gene regulatory networks has gained a special attention lately. To have a better understanding of these networks it is necessary to capture their dynamical properties, by computing its steady states. Various methods have been proposed to compute steady states but almost all of them suffer from the state space explosion problem with the increasing size of the networks. Hence it becomes difficult to model even moderate sized networks using these techniques. In this paper, we present a new representation of gene regulatory networks, which facilitates the steady state computation of networks as large as 1200 nodes and 5000 edges. We benchmarked and validated our algorithm on the T helper model from [8] and performed in silico knock out experiments: showing both a reduction in computation time and correct steady state identification.

Modeling of Multiple Valued Gene Regulatory Networks

In silico modeling of Gene Regulatory Networks has gained a lot of attention recently as it gives a very powerful tool to experimental biologists to gather the knowledge gained from different biological experiments and understand the dynamics of the overall system. One of the key dynamics that is often interesting is the steady states of the networks which biologically corresponds to the cellular states. In our previous paper, we gave an efficient method called GenYsis to compute these steady states in Boolean representation of Gene Regulatory Network. It has been observed that protein may be expressed at more then two level of expression. This may result in different cellular outcomes. To address this issue, we present here a multiple-level modeling methodology that allows us to be more accurate. In this paper we extend our software GenYsis to model gene regulatory networks where each node in the network may take multiple values.

Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells.

Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors—OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.

Dynamical spot queries to improve specificity in P450s based multi-drugs monitoring

Personalized therapy requires accurate and frequent monitoring of drugs metabolic response in living organisms during drug treatments. In case of high risk side effects, e.g. therapies with interfering anti-cancer molecules cocktails, direct monitoring of the patients drug metabolism is essential as the metabolic pathways efficacy is highly variable on a patient-by-patient basis. Moreover, anti-cancer pharmacological treatments are often based on cocktails of different drugs. Currently, there are no fully mature biochip systems to monitor multi-panel drugs amount in blood or in serum. The aim of this paper is to investigate the complexity of multiple drugs detection for point-of-care systems to be used in personalized therapy. Probes molecules for the biochip are the P450 enzymes as they have key role in drugs metabolism. Multiple drugs detection is carried out both by simulations and electrochemical experiments. Drugs specificity enhancement is investigated considering components decomposition of peak as registered in cyclic voltammetry acquisitions. This investigation has the aim to identify crucial aspect in VLSI design of fully-electronics biochip development in this field.

Implicit Methods for Qualitative Modeling of Gene Regulatory Networks

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.