Utilizing molecular data to derive functional physiological models tailored for specific cancer c... more Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for ...
... (Extended Abstract) Shlomi Reuveni1,2,⋆ , Isaac Meilijson1, Martin Kupiec3, Eytan Ruppin4,5, ... more ... (Extended Abstract) Shlomi Reuveni1,2,⋆ , Isaac Meilijson1, Martin Kupiec3, Eytan Ruppin4,5, and Tamir Tuller6,7,⋆,⋆⋆ ... Science 324, 218223 (2009) 3. dos Reis, M., et al.: Solving the riddle of codon usage preferences: a test for trans-lational selection. Nucleic Acids Res. ...
Proceedings of the National Academy of Sciences, 2013
Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype i... more Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.
One of the basic postulates of molecular evolution is that functionally important genes should ev... more One of the basic postulates of molecular evolution is that functionally important genes should evolve slower than genes of lesser significance. Essential genes, whose knockout leads to a lethal phenotype are considered of high functional importance, yet whether they are truly more conserved than nonessential genes has been the topic of much debate, fuelled by a host of contradictory findings. Here we conduct the first large-scale study utilizing genome-scale metabolic modeling and spanning many bacterial species, which aims to answer this question. Using the novel Media Variation Analysis, we examine the range of conservation of essential vs. nonessential metabolic genes in a given species across all possible media. We are thus able to obtain for the first time, exact upper and lower bounds on the levels of differential conservation of essential genes for each of the species studied. The results show that bacteria do exhibit an overall tendency for differential conservation of their...
We investigate the formation of a Hebbian cell assembly of spiking neurons, usinga temporal synap... more We investigate the formation of a Hebbian cell assembly of spiking neurons, usinga temporal synaptic learning curve that is based on recent experimental findings. Itincludes potentiation for short time delays between pre- and post-synaptic neuronalspiking, and depression for spiking events occurring in the reverse order. The couplingbetween the dynamics of synaptic learning and that of neuronal activation leads tointeresting results.
We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal s... more We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occuring in the reverse order. The coupling between the dynamics of the synaptic learning
We present an effectively computable measure of functional gene similarity that is based on metab... more We present an effectively computable measure of functional gene similarity that is based on metabolic gene activity across a vari- ety of growth media. We applied this measure to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to those obtained by using experimen- tal expression data, we show that our computational
Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and d... more Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and drugs, genetic factors, and interactions between the two. Previous experimental and computational studies of how environmental factors affect cellular metabolism were limited to the analysis of only a small set of growth media. In this study, we present a new computational method for predicting metabolic gene-nutrient interactions (GNI) that uncovers the dependence of gene essentiality on the presence or absence of nutrients in the growth medium. The method is based on constraint-based modeling, permitting the systematic exploration of a large putative growth media space. Applying this method to predict GNIs in the amino-acid metabolism system of yeast reveals complex interdependencies between amino-acid biosynthesis pathways. The predicted GNIs also enable the reverse-prediction of growth media composition, based on gene essentiality data. These results suggest that our approach may be applied to learn about the host environment in which a microorganism is embedded given data pertaining to gene lethality, providing a means for the identification of a species natural habitat.
Bioinformatics/computer Applications in The Biosciences, 2007
Motivation: Several recent studies attempted to establish measures for the similarity between gen... more Motivation: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. Results: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of
We use a neural network model to investigate how the interplay between synaptic deletion and comp... more We use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. We show that, in parallel with the experimental data, memory deterioration can be much delayed by strengthening the remaining synaptic weights. Using different dependencies of the compensatory strengthening on the amount of synaptic deletion various compensation strategies can be defined, corresponding to the observed ...
Growth rate has long been considered one of the most valuable phenotypes that can be measured in ... more Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale meta...
We study an Attractor Neural Network that stores natural concepts, organized iu semantic classes.... more We study an Attractor Neural Network that stores natural concepts, organized iu semantic classes. The concepts are related by both semantic and episodic associations. When neurons characterized by large synaptic connectivity are deleted, semantic transitions among concepts decay before the episodic ones, in accordance with the findings in patients with Alzheimer's disease.
Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leve... more Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect t...
Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) are associated wit... more Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) are associated with a highly malignant form of renal cancer. We combined analytical chemistry and metabolic computational modelling to investigate the metabolic implications of FH loss in immortalized and primary mouse kidney cells. Here, we show that the accumulation of fumarate caused by the inactivation of FH leads to oxidative stress that is mediated by the formation of succinicGSH, a covalent adduct between fumarate and glutathione. Chronic succination of GSH, caused by the loss of FH, or by exogenous fumarate, leads to persistent oxidative stress and cellular senescence in vitro and in vivo. Importantly, the ablation of p21, a key mediator of senescence, in Fh1-deficient mice resulted in the transformation of benign renal cysts into a hyperplastic lesion, suggesting that fumarate-induced senescence needs to be bypassed for the initiation of renal cancers.
High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinic... more High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner-treatments that reverse the transcr...
Proceedings of the National Academy of Sciences of the United States of America, Jan 12, 2014
A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level... more A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new envi...
Utilizing molecular data to derive functional physiological models tailored for specific cancer c... more Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for ...
... (Extended Abstract) Shlomi Reuveni1,2,⋆ , Isaac Meilijson1, Martin Kupiec3, Eytan Ruppin4,5, ... more ... (Extended Abstract) Shlomi Reuveni1,2,⋆ , Isaac Meilijson1, Martin Kupiec3, Eytan Ruppin4,5, and Tamir Tuller6,7,⋆,⋆⋆ ... Science 324, 218223 (2009) 3. dos Reis, M., et al.: Solving the riddle of codon usage preferences: a test for trans-lational selection. Nucleic Acids Res. ...
Proceedings of the National Academy of Sciences, 2013
Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype i... more Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.
One of the basic postulates of molecular evolution is that functionally important genes should ev... more One of the basic postulates of molecular evolution is that functionally important genes should evolve slower than genes of lesser significance. Essential genes, whose knockout leads to a lethal phenotype are considered of high functional importance, yet whether they are truly more conserved than nonessential genes has been the topic of much debate, fuelled by a host of contradictory findings. Here we conduct the first large-scale study utilizing genome-scale metabolic modeling and spanning many bacterial species, which aims to answer this question. Using the novel Media Variation Analysis, we examine the range of conservation of essential vs. nonessential metabolic genes in a given species across all possible media. We are thus able to obtain for the first time, exact upper and lower bounds on the levels of differential conservation of essential genes for each of the species studied. The results show that bacteria do exhibit an overall tendency for differential conservation of their...
We investigate the formation of a Hebbian cell assembly of spiking neurons, usinga temporal synap... more We investigate the formation of a Hebbian cell assembly of spiking neurons, usinga temporal synaptic learning curve that is based on recent experimental findings. Itincludes potentiation for short time delays between pre- and post-synaptic neuronalspiking, and depression for spiking events occurring in the reverse order. The couplingbetween the dynamics of synaptic learning and that of neuronal activation leads tointeresting results.
We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal s... more We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occuring in the reverse order. The coupling between the dynamics of the synaptic learning
We present an effectively computable measure of functional gene similarity that is based on metab... more We present an effectively computable measure of functional gene similarity that is based on metabolic gene activity across a vari- ety of growth media. We applied this measure to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to those obtained by using experimen- tal expression data, we show that our computational
Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and d... more Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and drugs, genetic factors, and interactions between the two. Previous experimental and computational studies of how environmental factors affect cellular metabolism were limited to the analysis of only a small set of growth media. In this study, we present a new computational method for predicting metabolic gene-nutrient interactions (GNI) that uncovers the dependence of gene essentiality on the presence or absence of nutrients in the growth medium. The method is based on constraint-based modeling, permitting the systematic exploration of a large putative growth media space. Applying this method to predict GNIs in the amino-acid metabolism system of yeast reveals complex interdependencies between amino-acid biosynthesis pathways. The predicted GNIs also enable the reverse-prediction of growth media composition, based on gene essentiality data. These results suggest that our approach may be applied to learn about the host environment in which a microorganism is embedded given data pertaining to gene lethality, providing a means for the identification of a species natural habitat.
Bioinformatics/computer Applications in The Biosciences, 2007
Motivation: Several recent studies attempted to establish measures for the similarity between gen... more Motivation: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. Results: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of
We use a neural network model to investigate how the interplay between synaptic deletion and comp... more We use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. We show that, in parallel with the experimental data, memory deterioration can be much delayed by strengthening the remaining synaptic weights. Using different dependencies of the compensatory strengthening on the amount of synaptic deletion various compensation strategies can be defined, corresponding to the observed ...
Growth rate has long been considered one of the most valuable phenotypes that can be measured in ... more Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale meta...
We study an Attractor Neural Network that stores natural concepts, organized iu semantic classes.... more We study an Attractor Neural Network that stores natural concepts, organized iu semantic classes. The concepts are related by both semantic and episodic associations. When neurons characterized by large synaptic connectivity are deleted, semantic transitions among concepts decay before the episodic ones, in accordance with the findings in patients with Alzheimer's disease.
Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leve... more Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect t...
Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) are associated wit... more Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) are associated with a highly malignant form of renal cancer. We combined analytical chemistry and metabolic computational modelling to investigate the metabolic implications of FH loss in immortalized and primary mouse kidney cells. Here, we show that the accumulation of fumarate caused by the inactivation of FH leads to oxidative stress that is mediated by the formation of succinicGSH, a covalent adduct between fumarate and glutathione. Chronic succination of GSH, caused by the loss of FH, or by exogenous fumarate, leads to persistent oxidative stress and cellular senescence in vitro and in vivo. Importantly, the ablation of p21, a key mediator of senescence, in Fh1-deficient mice resulted in the transformation of benign renal cysts into a hyperplastic lesion, suggesting that fumarate-induced senescence needs to be bypassed for the initiation of renal cancers.
High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinic... more High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner-treatments that reverse the transcr...
Proceedings of the National Academy of Sciences of the United States of America, Jan 12, 2014
A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level... more A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new envi...
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