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bioRxiv2018

Decrypting protein surfaces by combining evolution, geometry and molecular docking

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a proteins surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physico-chemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.\n\nAuthor summaryThe multiplicity and versatility of protein interactions make protein surfaces complex biological objects. For instance, a protein may interact with several partners at different moments via the same region at its surface, or it may use several regions, with different shapes and evolutionary origins, to establish different types of interactions. In addition, interfaces can re-adjust depending on environmental conditions. In this work, we introduce the notion of interaction region\", in contrast to interaction site\", which accounts for the interface variability coming from molecular flexibility and for the multiple usage of the protein surface by several partners. Moreover, we use four biologically meaningful descriptors to delineate protein surface patches. Three of them, namely evolutionary conservation, physico-chemical composition and local geometry, are computed for a given protein, without any knowledge on its potential partners. The fourth property is inferred from the behavior of the protein with respect to other proteins, partners or not, in docking simulations. We show that predicted patches match well with known interacting regions, that the four descriptors are complementary and that they enable capturing most of the signal relevant to protein interactions. We further exploit the way patches are grown to precisely localize interaction regions, and to estimate whether a protein have a few or many partners.

bioinformatics↗

bioRxiv2018

MasterPATH: network analysis of functional genomics screening data

Functional genomics employs several experimental techniques to investigate gene functions. These techniques such as loss-of-function screening and transcriptome profiling performed in a high-throughput manner give as result a list of genes involved in the biological process of interest. There exist several computational methods for analysis and interpretation of the list. The most widespread methods aim at investigation of biological processes significantly represented in the list or at extracting significantly represented subnetworks. Here we present a new exploratory network analysis method that employs the shortest path approach and centrality measure to uncover members of active molecular pathways leading to the studied phenotype based on the results of functional genomics screening data. We present the method and we demonstrate what data can be retrieved by its application to the terminal muscle differentiation miRNA loss-of-function screening and transcriptomic profiling data and to the druggable loss-of-function RNAi screening data of the DNA repair process.

systems biology↗

bioRxiv2018

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

cancer biology↗

bioRxiv2018

Distinct pathogenic genes causing intellectual disability and autism exhibit overlapping effects on neuronal network development

An intriguing question in medical biology is how mutations in functionally distinct genes can lead to similar clinical phenotypes. For example, patients with mutations in distinct epigenetic regulators EHMT1, MBD5, MLL3 or SMARCB1 share the core clinical features of intellectual disability (ID), autism spectrum disorder (ASD) and facial dysmorphisms. To elucidate how these phenotypic similarities are reflected by convergence at the molecular, cellular and neuronal network level, we directly compared the effects of their loss of function in neurons. Interestingly, knockdown of each gene resulted in hyperactive neuronal networks with altered patterns of synchronized activity. At the single-cell level, we found genotype-specific changes in intrinsic excitability and excitatory-inhibitory balance, but in all cases leading to increased excitability. Congruent with our physiological findings, we identified dysregulated genes that converge on biological and cellular pathways related to neuronal excitability and synaptic function, including genes previously implicated in ID/ASD. Yet, our data suggests that the common cellular phenotypes depend on the ensemble of dysregulated genes engaged in neuronal excitability rather than the direction of transcriptional changes of individual genes. The demonstration of increasing convergence from molecular pathways to neuronal networks may be a paradigm for other types of ID/ASD.

neuroscience↗

bioRxiv2018

Quantitative Assessment of Protein Activity in Orphan Tissues and Single Cells Using the metaVIPER Algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator protein, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, a novel algorithm designed to assess protein activity in tissue-independent by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactome. We confirmed the algorithms value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

systems biology↗

bioRxiv2016

A neuropeptide modulates sensory perception in the entomopathogenic nematode Steinernema carpocapsae

Entomopathogenic nematodes (EPNs) employ a sophisticated chemosensory apparatus to detect potential hosts. Understanding the molecular basis of relevant host-finding behaviours could facilitate improved EPN biocontrol approaches, and could lend insight to similar behaviours in economically important mammalian parasites. FMRFamide-like peptides are enriched and conserved across the Phylum Nematoda, and have been linked with motor and sensory function, including dispersal and aggregating behaviours in the free living nematode Caenorhabditis elegans. The RNA interference (RNAi) pathway of Steinernema carpocapsae was characterised in silico, and employed to knockdown the expression of the FMRFamide-like protein 21 (GLGPRPLRFamide) gene (flp-21) in S. carpocapsae infective juveniles; a first instance of RNAi in this genus, and a first in an infective juvenile of any EPN species. Our data show that 5 mg/ml dsRNA and 50 mM serotonin triggers statistically significant flp-21 knockdown (-84%***) over a 48 h timecourse, which inhibits host-finding (chemosensory), dispersal, hyperactive nictation and jumping behaviours. However, whilst 1 mg/ml dsRNA and 50 mM serotonin also triggers statistically significant flp-21 knockdown (-51%**) over a 48 h timecourse, it does not trigger the null sensory phenotypes; statistically significant target knockdown can still lead to false negative results, necessitating appropriate experimental design. SPME GC-MS volatile profiles of two EPN hosts, Galleria mellonella and Tenebrio molitor reveal an array of shared and unique compounds; these differences had no impact on null flp-21 RNAi phenotypes for the behaviours assayed. Localisation of flp-21 / FLP-21 to paired anterior neurons by whole mount in situ hybridisation and immunocytochemistry corroborates the RNAi data, further suggesting a role in sensory modulation. These data can underpin efforts to study these behaviours in other economically important parasites, and could facilitate molecular approaches to EPN strain improvement for biocontrol.\n\nAuthor summaryEntomopathogenic nematodes (EPNs) use a range of behaviours in order to find a suitable host, some of which are shared with important mammalian parasites. The ethical burden of conducting research on parasites which require a mammalian host has driven a move towards appropriate model parasites, like EPNs, which have short life cycles, can be cultured in insects or agar plates, and have excellent genomic resources. This study aimed to develop a method for triggering gene knockdown by RNA interference, a biochemical pathway involved in gene regulation. Through knocking down the expression of a target gene we can then study the function of that gene, helping us to understand the molecular basis of behaviour. Here we have characterised the RNAi pathway of Steinernema carpocapsae through analysing the genome sequence for relevant genes, and have successfully knocked down the neuropeptide gene flp-21 in S. carpocapsae infective juveniles. We find that it is involved in the regulation of behaviours which rely on sensory perception and relate to host-finding. We have localised the gene and mature neuropeptide, and find them to be expressed in paired anterior neurons, which is in broad agreement with our behavioural observations following RNAi. Our observations are relevant to interactions of S. carpocapsae with two insect hosts, the waxworm Galleria mellonella, and the meelworm, Tenebrio molitor. We identified the volatile compounds relating to both insects, and find that there are both shared and unique compounds to both species; EPNs use volatile compound gradients, as well as other physical cues in order to find and invade a host. This study provides a method for employing RNAi in a promising model parasite, and characterises the molecular basis of host-finding behaviours which could be relevant to economically important mammalian parasites. EPNs are also used as bioinsecticides, and so understanding their behaviour and biology could have broad benefits across industry and academia.

Animal Behavior and Cognition↗

bioRxiv2016

An RNA binding polymer specifies nematode sperm fate

Metazoan germ cells develop as sperm or oocytes, depending on chromosomal sex, extrinsic signaling from somatic tissue and intrinsic factors within the germ cells. Gamete fate regulatory networks have been analyzed in nematodes, flies and mammals, but only in C. elegans have terminal intrinsic regulators been identified, which include a Tob/BTG protein family member, FOG-3. Canonical Tob/BTG proteins function as monomeric adaptor proteins that link RNA binding proteins to deadenylases. To ask if FOG-3 functions similarly, we first determined its crystal structure. FOG-3 harbors a classical Tob/BTG fold, but unlike other Tob/BTG proteins, FOG-3 dimerizes and these FOG-3 dimers assemble into polymers. The importance of FOG-3 polymers to sperm fate specification was confirmed in vivo using CRISPR/Cas9 gene editing to create mutations designed to disrupt the polymer interface. The FOG-3 surface potential is highly basic, suggesting binding to nucleic acid. We find that FOG-3 binds RNA directly with a strong preference for 3UTRs of oogenic mRNAs. Our results reveal a divergent but striking molecular assembly for proteins with a Tob/BTG fold, make key advances in understanding the mechanism of sperm fate specification and highlight the potential for undiscovered protein polymers in biology.

developmental biology↗

bioRxiv2017

Independent erosion of conserved transcription factor binding sites points to shared hindlimb, vision, and scrotum loss in different mammals

Genetic variation in cis-regulatory elements is thought to be a major driving force in morphological and physiological change. However, identifying transcription factor binding events which code for complex traits remains a challenge, motivating novel means of detecting putatively important binding events. Using a curated set of 1,154 high-quality transcription factor motifs, we demonstrate that independently eroded binding sites are enriched for independently lost traits in three distinct pairs of placental mammals. We show that these independently eroded events pinpoint the loss of hindlimbs in dolphin and manatee, degradation of vision in naked mole-rat and star-nosed mole, and the loss of scrotum in white rhinoceros and Weddell seal. Our study exhibits a novel methodology to detect cis-regulatory mutations which help explain a portion of the molecular mechanism underlying complex trait formation and loss.\n\nAuthor SummaryEvolution has produced an astounding variety of species with incredibly diverse phenotypes. A central question in evolutionary developmental biology is how (and which) DNA evolves to encode all of these different traits. A prevailing hypothesis is that changes in regulatory DNA, short stretches of DNA which control the expression of protein-coding genes, drive important differences in trait formation between species. The basic building block of regulatory DNA is thought to be transcription factor binding sites, shortl genomic sequences which attract proteins whose central role is to control the rate of transcription. In this study, we asked whether the independent erosion of otherwise highly conserved transcription factor binding sites points to a trait shared between species which have undergone similar adaptations. We show that our method is able to point to the loss of hindlimbs in dolphin and manatee, poor vision in naked mole-rat and star-nosed mole, and loss of scrotum in Weddell seal and white rhinoceros. Overall, our study exhibits a means of detecting evolutionarily important genomic regions which help explain a portion of complex trait loss and retention.

evolutionary biology↗

bioRxiv2017

Protein Moonlighting Revealed by Non-Catalytic Phenotypes of Yeast Enzymes

A single gene can partake in several biological processes, and therefore gene deletions can lead to different--sometimes unexpected--phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene-deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, 30% could be readily complemented by a catalytically-inactive enzyme. Such non-catalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine-biosynthetic enzymes Ilv1 and Ilv2, suggesting novel \"moonlighting\" activities in these proteins. Furthermore, differential genetic-interaction profiles of gene-deletion and catalytic mutants indicated that ILV1 is functionally associated to regulatory processes, specifically to chromatin modification. Our systematic study shows that gene-loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.

genetics↗

bioRxiv2018

Multi-omic tumor data reveal diversity of molecular mechanisms underlying survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ( multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

cancer biology↗

bioRxiv2018

Directed Bayesian Networks established functional differences between breast cancer subtypes

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which has a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 106 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as metastasis or proliferation between breast cancer subtypes, including our new subtype TN-like. In addition, one of the components, mainly related with metastasis, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.\n\nAuthor SummaryBreast cancer classification in the clinical practice is defined by three biomarkers (estrogen receptor, progesterone receptor and HER2) into hormone receptor positive, HER2+ and triple negative breast cancer (TNBC). Our group recently described a new ER+ subtype with molecular characteristics and prognosis similar to TNBC. In this study we propose a mathematical method, the Bayesian networks, as a useful tool to study protein interactions and differential biological processes in breast cancer subtypes, characterizing differences in relevant processes such as proliferation or metastasis and associated them with patient prognosis.

cancer biology↗

bioRxiv2018

PGC1a and Exercise Adaptations in Zebrafish

Fish species display huge differences in physical activity ranging from lethargy to migration of thousands of miles, making them an interesting model to identify determinants of physical fitness. Here, we show a remarkable plasticity of zebrafish in response to exercise and induction of PGC1 (encoded by PPARGC1A), a dominant regulator of mitochondrial biogenesis. Forced expression of human PPARGC1A induces mitochondrial biogenesis, an exercise-like gene expression signature, and physical fitness comparable to wild-type animals trained in counter-current swim tunnels. Quantifying transcriptional and proteomic changes in response to exercise or PGC1, we identify conserved exercise adaptations, including a stoichiometric induction of the electron transport chain (ETC) that re-organizes into respiratory supercomplexes in both conditions. We further show that ndufa4/ndufa4l, previously assigned to complex I, associates to free and supramolecular complex IV in vivo. Thus, zebrafish is a useful and experimentally tractable vertebrate model to study exercise biology, including ETC expression and assembly.\n\nHIGHLIGHTSO_LIPGC1 reprograms zebrafish skeletal muscle to a red fiber phenotype and increases exercise performance\nC_LIO_LIZebrafish show a high molecular plasticity in response to PGC1 and exercise\nC_LIO_LISWATH-MS proteomics show a stoichiometric induction of the electron transport chain that organizes as supercomplexes in response to PGC1 and exercise\nC_LIO_LIndufa4/ndufa4l associate to free and supramolecular complex IV in vivo\nC_LI

physiology↗

bioRxiv2017

Lessons in effector and NLR biology of plant-microbe systems

A diversity of plant-associated organisms secrete effectors--proteins and metabolites that modulate plant physiology to favor host infection and colonization. However, effectors can also activate plant immune receptors, notably nucleotide-binding domain and leucine-rich repeat-containing (NLR) proteins, enabling plants to fight off invading organisms. This interplay between effectors, their host targets, and the matching immune receptors is shaped by intricate molecular mechanisms and exceptionally dynamic coevolution. In this article, we focus on three effectors, AVR-Pik, AVR-Pia, and AVR-Pii, from the rice blast fungus Magnaporthe oryzae (syn. Pyricularia oryzae), and their corresponding rice NLR immune receptors, Pik, Pia, and Pii, to highlight general concepts of plant-microbe interactions. We draw 12 lessons in effector and NLR biology that have emerged from studying these three little effectors and are broadly applicable to other plant-microbe systems.

plant biology↗

bioRxiv2018

Deciphering noise amplification and reduction in open chemical reaction networks

The impact of fluctuations on the dynamical behavior of complex biological systems is a longstanding issue, whose understanding would elucidate how evolutionary pressure tends to modulate intrinsic noise. Using the It[o] stochastic differential equation formalism, we performed analytic and numerical analyses of model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. For networks of zero deficiency, which admit a detailed- or complex-balanced steady state, all molecular species are uncorrelated and their Fano factors are Poissonian. Systems of higher deficiency have non-equilibrium steady states and non-zero reaction fluxes flowing between the complexes. When they model homooligomerization, the noise on each species is reduced when the flux flows from the oligomers of lowest to highest degree, and amplified otherwise. In the case of hetero-oligomerization systems, only the noise on the highest-degree species shows this behavior.

systems biology↗

bioRxiv2016

Live Imaging of muscle histolysis in Drosophila metamorphosis

BackgroundThe contribution of programmed cell death (PCD) to muscle wasting disorders remains a matter of debate. Drosophila melanogaster metamorphosis offers the opportunity to study muscle cell death in the context of development. Using live cell imaging of the abdomen, two groups of larval muscles can be observed, doomed muscles that undergo histolysis and persistent muscles that are remodelled and survive into adulthood.\n\nMethodTo identify and characterize genes that control the decision between survival and cell death of muscles, we developed a method comprising in vivo imaging, targeted gene perturbation and time-lapse image analysis. Our approach enabled us to study the cytological and temporal aspects of abnormal cell death phenotypes.\n\nResultsIn a previous genetic screen for genes controlling muscle size and cell death in metamorphosis, we identified gene perturbations that induced cell death of persistent or inhibit histolysis of doomed larval muscles. RNA interference (RNAi) of the genes encoding the helicase Rm62 and the lysosomal Cathepsin-L homolog Cysteine proteinase 1 (Cp1) caused premature cell death of persistent muscle in early and mid-pupation, respectively. Silencing of the transcriptional co-repressor Atrophin inhibited histolysis of doomed muscles. Overexpression of dominant-negative Target of Rapamycin (TOR) delayed the histolysis of a subset of doomed and induced ablation of all persistent muscles. RNAi of AMPK, which encodes a subunit of the AMPK protein complex that senses AMP and promotes ATP formation, led to loss of attachment and a spherical morphology. None of the perturbations affected the survival of newly formed adult muscles, suggesting that the method is useful to find genes that are crucial for the survival of metabolically challenged muscles, like those undergoing atrophy. The ablation of persistent muscles did not affect eclosion of adult flies.\n\nConclusionsLive imaging is a versatile approach to uncover gene functions that are required for the survival of muscle undergoing remodelling, yet are dispensable for other adult muscles. Our approach promises to identify molecular mechanisms that can explain the resilience of muscles to PCD.

Developmental Biology↗

bioRxiv2017

Clingen Cancer Somatic Working Group: standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLMs ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGens Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

bioinformatics↗

bioRxiv2017

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends the molecular diagnosis of the IDH mutational status in gliomas. IDH-mutant gliomas manifest the CpG Island Methylator Phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provide a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.\n\nConflict of interestThe authors declare no conflict of interest

cancer biology↗

bioRxiv2015

Landauer in the age of synthetic biology: energy consumption and information processing in biochemical networks

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using \"post-translational\" synthetic biology that exploits reversible protein modification.

Synthetic Biology↗

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