Workshop 1: Metabolic Engineering

(September 24,2007 - September 28,2007 )

Organizers


John Doyle
Control & Dynamical Systems, California Institute of Technology
David Gang
Department of Plant Sciences, University of Arizona

Broadly defined, metabolic engineering seeks to change the metabolism and physiology of an organism to suit the needs or desires of the farmer, the breeder, the genetic engineer, and the scientist. Targeted selection for more flavorful wines , for higher milk production in cattle, for larger chicken breasts, for sweeter corn, and for larger and more flavorful apples are all examples of metabolic engineering products that have been largely successful. In all of these instances, the metabolism of the organism was altered in such a way as to allow that organism to display the desired traits. However, such breeding-program driven projects are very slow to produce results and often end in failure. The exact changes in the organism that result in the altered phenotype are often unknown, making reproduction of the same changes in these or similar organisms almost impossible.

Although metabolic engineering of plants and microbes is a major scientific activity today, there are numerous biological and, increasingly, mathematical challenges. One can organize the challenges of metabolic engineering roughly into four areas: measurement technologies (sensing and quantification) for generating data and monitoring system performance; mathematical modeling (formulation, verification, and analysis) for systematic representation and characterization of the system; molecular tools (actuators and regulators) for altering the system in a controlled fashion; and system integration (system [re]design, prediction, and control) for discovery of system design principles and rational optimization. Advances in one area are obviously dependent on those in the others. New developments in each of these areas will form the interrelated themes of this workshop. Examples from microbes and plants will be emphasized.

The workshop will be organized along the following outline:

  1. Overview of Organisms, Biological Tools, and Strategies: Microbes, plants and animals, mutagenesis, knockout and transfer of genes, rational design and directed evolution.
  2. Measurement Technologies and Data Analysis Tools: Metabolites and fluxes, mRNA, protein, miRNA.
  3. Mathematical Modeling: Metabolic pathways, protein interactions, gene circuitry.
  4. Molecular Tools: Enzyme design, rewired circuitry, de novo circuitry, biological computing.
  5. Systems Organization and Integration: Enzymatic networks, gene circuits, miRNA speculations, robust design and control.

Accepted Speakers

Mark Brynildsen
Chemical and Biomolecular Engineering, University of California, Los Angeles
Drew Endy
Biological Engineering Division, Massachusetts Institute of Technology
Oliver Fiehn
Genome Center And Bioinformatics Program, University of California, Davis
Larry Gold
CEO & Chairman of the Board, SomaLogic, Inc.
Erich Grotewold
Plant Biology and Plant Biotechnology Center, The Ohio State University
Elmar Heinzle
Biochemical Engineering, Universit""at des Saarlandes
Terence Hwa
Physics, University of California, San Diego
Jay Keasling
Department of Chemical Engineering, University of California, Berkeley
Paul O'Maille
The Salk Institute for Biological Studies, University of California, San Diego
Howard Salis
University of California - San Francisco
Armindo Salvador
Molecular Systems Biology Group, University of Coimbra
Christina Smolke
Chemical Engineering, California Institute of Technology
Brian Tjaden
Computer Science, Wellesley College
Eberhard Voit
Dept. of Biomedical Engineering, Georgia Tech and Emory University
Ying Xu
Biochemistry and Molecular Biology, University of Georgia
Monday, September 24, 2007
Time Session
09:00 AM
10:00 AM
Erich Grotewold - Metabolic engineering: Where we are and what are the main issues today

Metabolic engineering has been defined as "the improvement of cellular activities by the manipulation of enzymatic, transport, and regulatory functions of the cell with the use of recombinant DNA technologies." The elucidation of the genome sequences for many microbes, fungi, animals and plants has provided a number of unique tools to tackle the challenge of engineering metabolic pathways as part of interdisciplinary efforts that integrate biology and chemistry with engineering and mathematics. However, fundamental issues remain, such as the adverse social reaction to the utilization of genetically modified organisms (GMO), the difficulties associated with predicting the effect of genetic manipulations on the metabolome, and the problems associated with targeting metabolites to the desired cellular or sub-cellular locations. These and other issues will be described, putting metabolic engineering within the perspective of alternatives (such as chemical synthesis) with the goal to provide a perspective of the opportunities and future of the field for interdisciplinary interactions.



 
10:30 AM
11:30 AM
David Gang - Mutagenesis, knockout, and transfer of genes

N/A

03:30 PM
04:30 PM
Larry Gold - The Plasma Proteome: An Integrator of Human Biochemical Systems Analysis?

Human biology functions through (largely unknown and) bewildering sets of interacting molecules. Even the parts list is incomplete - while we know that roughly 23,000 human proteins are expressed, each protein can be changed by alternative splicing, variable post-translational adducts, and protein processing. Perhaps the number of different human proteins is a couple hundred thousand, but no one really knows. The situation for RNA is worse - it is likely that in human cells every possible RNA is expressed at a low level, adding another 60,000,000 non-overlapping RNA 100-mers to the mix. If one loves the idea of a pre-biotic RNA World, one would be foolish to discount the possible functions available today in this deep human RNA sequence space. Bacteria have smaller genomes and parts lists, and have had more "genetics" - between faster generation times and higher mutation rates, bacteria are engineered today through a more complete Darwinian process than has been possible for mammals.


Thinking this way raises a question for metabolic engineering - should one design or evolve novel biochemical systems? Clunky mammals and less-clunky bacteria suggest that evolution strategies should be tried.


The deepest combinatorial selection paradigm (SELEX) has provided an astonishing set of reagents (aptamers), and at the same time provided lessons for how to manage evolution/selective strategies. Even though I'd like to lecture about the human plasma proteome (the main interest at SomaLogic), I will focus my talk on the possible value of in vitro and in vivo selections aimed at the discovery of novel biochemical systems.

Tuesday, September 25, 2007
Time Session
09:00 AM
10:00 AM
Terence Hwa - Quantitative characteristics of gene regulation by small RNA

An increasing number of small RNAs (sRNA) have been shown to regulate critical pathways in prokaryotes and eukaryotes. In bacteria, sRNA regulation is predominantly involved in coordinating intricate stress responses. The mechanisms by which sRNA modulate expression of its targets are diverse. In common to most is the possibility that the level of functional sRNA may be altered via its interaction with its targets. Aiming to understand the unique role played by sRNAs, we study quantitatively two classes of bacterial sRNAs in Escherichia coli using a combination of experimental and theoretical approaches. Our results demonstrate that sRNA provides a novel mode of gene regulation, with characteristics distinct from those of protein-mediated gene regulation. These include a threshold-linear response with a tuneable threshold, a robust noise resistance characteristic, and a built-in capability for hierarchical cross talk. Knowledge of these special features of sRNA-mediated regulation is crucial towards understanding the subtle functions that sRNAs play in coordinating various stress-relief pathways, and can help guide the design of synthetic genetic circuits with properties difficult to attain with protein regulators alone.



 
10:30 AM
11:30 AM
Christina Smolke - A framework for programming integrated RNA devices

Recent progress in developing frameworks for the construction of RNA devices is enabling rapid advances in cellular engineering applications. These devices provide scalable platforms for the construction of molecular communication and control systems for reporting on, responding to, and controlling intracellular components in living systems. Research that has demonstrated the modularity, portability, and specificity inherent in these molecules for cellular control will be highlighted and its implications for synthetic and systems biology research will be discussed. In addition, new tools that translate sequence information to device function to enable the forward design and optimization of new devices will be discussed. The flexibility of the specified framework enables these molecules to be integrated as systems that perform higher-level signal processing based on molecular computation strategies. The application of these molecular devices to studying cellular systems through non-invasive /in vivo /monitoring of biomolecule levels and to regulating cellular behavior, in particular in the control and optimization of biosynthesis, will be discussed.



 
02:00 PM
03:00 PM
Mark Brynildsen - Analysis of transcription networks in E. coli

N/A

Wednesday, September 26, 2007
Time Session
09:00 AM
10:00 AM
Paul O'Maille - Basic science driving protein engineering: Questions shape the tools

Advances in structural and molecular biology have spurred the proliferation of protein engineering technologies, allowing fundamental questions about protein evolution to become approachable. The questions themselves, in turn, can be the drivers for the development of new tools. I will describe how my interests in protein evolution shaped the development of structure-based combinatorial protein engineering (SCOPE), a tool for connecting evolutionary endpoints in local and global sequence space. I will first discuss the inception of SCOPE as a homology-independent recombination method and its application to create multiple-crossover libraries from distantly-related DNA polymerases, and then describe adapting the technique for combinatorial mutagenesis to recapitulate the more recent functional divergence of closely-related terpene cyclases. Throughout my discussions, I will highlight probabilistic considerations of library design, detail key experimental results and what they tell us about the evolution of specialized metabolism and implications for metabolic engineering.



 
10:30 AM
11:30 AM
Oliver Fiehn - Stress Response Metabolism in Chlamydomonas reinhardtii

The green alga Chlamydomonas reinhardtii is a good biological model to study fundamental biological questions ranging from motility, photosynthesis to metabolism. As a photosynthetic cell, Chlamydomonas thrives in minimal media, carbon dioxide and light, but it can also grow heterotrophically on acetate. Unlike yeast or E.coli, any metabolite detected in the intra- or extracellular space is therefore a product of its enzymatic machinery, since only inorganic nutrients are needed. In addition, cell division of Chlamydomonas can be synchronized by light periods, so at any given time >95% of all cells in a culture are identical. Chlamydomonas batch cultures enable pursuing multiple experiments with limited efforts in small time frames, yet high analytical accuracy and precision.


We have utilized Chlamydomonas to study its metabolic responses under nitrogen depletion time courses using four different levels of N-supply and four different time points of stress response, each with eight independent replicates. Metabolite profiles were analyzed using improved methods that had median technical errors of 16% CV on 80 identified compounds using 5x10^6 cells per sample. Data were processed using an automated database approach and data were analyzed in a first pass using classic univariate and multivariate statistics. The dominant effect on metabolic variance was found to be depending on cell cycles states which are controlled by putrescine metabolism in this organism. In order to further the underlying understanding of additional metabolic perturbations by the physiological stress treatment, data were mean centered to the standard N-condition controls and analyzed for biochemical changes. Surprisingly, large and non-uniform differences could be observed for both dose and time of N-depletion on a wide number of metabolites. Data analysis was complemented by analyzing metabolic networks using Likelynet, a Bayesian likelihood method that is geared towards unbiased detection and verification of linear relationships in metabolic datasets, taking into account the technical error estimates for each variable (Kose F. et al, BMC Bioinformatics 2007, 8:162 http://www.biomedcentral.com/1471-2105/8/162 ).


Work done in collaboration with Do Yup Lee, Jan Budzcies, and Frank Kose.

02:00 PM
03:00 PM
Elmar Heinzle - Fluxes - quantifying flows in metabolic pathways

In a first part of my talk I will briefly review relevant methods for metabolic flux analysis. This includes metabolite balancing and flux analysis using labelled substrates with necessary experimental and computational methods. I will finish my talk with two recent case studies:



  1. Metabolic fluxes of a plant secondary metabolite pathway. In this case study dynamic labelling experiments were used to elucidate pathway fluxes in native potatoe and after addition of an elucidator.

  2. Regulation of central metabolic fluxes in Bacillus subtilis. Fluxes in various mutants of B. subtilis using different substrate combinations were determined after model based experimental planning.

03:30 PM
04:30 PM
Brian Tjaden - Characterizing noncoding RNA genes in bacteria

Small noncoding RNAs are genes for which RNA, rather than protein is the functional end product. In bacteria, many small RNA genes (sRNAs) appear to act as post-transcriptional regulators by basepairing with target messenger RNAs. In this talk, we will look at computational and experimental approaches to characterize these sRNA genes in bacteria. First, we will describe high-throughput approaches for identifying sRNA genes in a bacterial genome. In particular, we will consider a probabilistic model that combines heterogeneous data sources (including primary sequence data, comparative genomics information, and microarray expression data) for the purpose of predicting sRNA genes throughout a genome. We will then investigate methods, both computational and experimental, for characterizing regulatory targets of sRNA action. Finally, we will explore how these high-throughput approaches are used to elucidate the roles of specific sRNA genes and the pathways in which the genes are involved.



 
Thursday, September 27, 2007
Time Session
10:30 AM
11:30 AM
Eberhard Voit - Estimation of Metabolic Model Parameters from Time Series Data

Stoichiometric approaches have been tremendously successful as mathematical models in metabolic engineering. Their linearity permits an unparalleled repertoire of mathematical and computational tools, and the combination of stoichiometric models with experimental data has yielded valuable insights into flux distributions under different conditions. However, as we strive to understand the details of control and regulation in vivo at a deeper level, refined models are needed, and these must be nonlinear. While simulations with nonlinear metabolic models are no longer a significant computational hurdle, the estimation of suitable parameter values continues to be a major challenge. In this presentation I will review current approaches to metabolic parameter estimation, especially for time series data, and demonstrate why it is important to obtain fast solutions on standard computers. As an example for many aspects of my presentation, I will use the regulation of glucose utilization in Lactococcus lactis, for which we have high-precision in vivo data describing the dynamics of intracellular metabolite pools.



  1. Voit, E.O.: Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists, xii + 530 pp., Cambridge University Press, Cambridge, U.K., 2000.

  2. Voit, E.O., J.S. Almeida, S. Marino, R. Lall, G. Goel, A.R. Neves, and H. Santos. Regulation of Glycolysis in Lactococcus lactis: An Unfinished Systems Biological Case Study. IEE Proc. Systems Biol. 153, 286-298, 2006.

  3. Voit, E.O., A.R. Neves, and H. Santos. The Intricate Side of Systems Biology. PNAS, 103(25), 9452-9457, 2006.

  4. Goel, G., I-Chun Chou, and E.O. Voit: Biological Systems Modeling and Analysis: A Biomolecular Technique of the 21st Century. J. Biomolec. Techn. 17, 252-269, 2006.

02:00 PM
03:00 PM
Ying Xu - Gene circuitry (inferring natural circuits in bacteria)

N/A

03:30 PM
04:30 PM
Mustafa Khammash - Stochastic gene expression

The cellular environment is abuzz with noise. Generated by random molecular events, cellular noise not only results in random fluctuations within individual cells but it is also a source of phenotypic variability among clonal cellular populations. In some instances fluctuations are suppressed downstream through an intricate dynamical network that acts to filter the noise. Yet in other instances, noise induced fluctuations are exploited to the cell's advantage. Intriguing mechanisms that rely on noise include stochastic switches, coherence resonance in oscillators, and stochastic focusing. While mathematical models of genetic networks often represents gene expression and regulation as deterministic processes with continuous variables, the stochastic nature of cellular noise necessitates an approach that models these variables as discrete and stochastic. In this framework, probability densities of the system states evolve according to a (usually infinite dimensional) Chemical Master Equation (CME). Until recently, sample trajectories have been computed almost exclusively with Kinetic Monte Carlo methods, such as Gillespie's Stochastic Simulation Algorithm. In this talk we present a new direct approach for computing the relevant statistics, which involves the projection of the solution of the CME onto finite subsets. We illustrate the algorithm underlying our Finite State Projection approach and introduce a variety of systems theory based modifications and enhancements that enable large reductions and increased efficiency with little to no loss in accuracy. Model reduction techniques based on linear perturbation theory allow for the systematic projection of multiple time scale dynamics onto a slowly varying manifold of much smaller dimension. The proposed projection approach is illustrated on few important models of genetic regulatory networks.

09:00 PM
10:00 AM
Armindo Salvador - Design principles of moiety supply units in metabolic networks

Metabolic networks have a bow-tie architecture: a wide diversity of nutrients is disassembled into a few molecular currencies, which are then reassembled into a large variety of other molecules. At the "knot" of this bowtie lie cycles whereby a set of reactions transfer a molecular group (moiety) from various donor metabolites to a common carrier (metabolic currency) from which another set of reactions transfers the moiety to various accepting metabolites. These circuits couple moiety supply to demand. Their role, and the performance criteria they should fulfill, are akin to those of a power-supply unit in an electronic circuit.


The first part of this talk will explore the design principles enabling this general class of metabolic circuits to operate effectively as "moiety-supply units". The second part of the talk will examine quantitative aspects of the design of concrete biological realizations of these circuits.

Friday, September 28, 2007
Time Session
09:00 AM
10:00 AM
Drew Endy - Engineering biology (making some rules)

N/A

10:30 AM
11:30 AM
Howard Salis - System design (assembling novel systems)

Bacteria are tiny chemical factories that can sense their environment and react to stimuli according to a pre-programmed response. These responses are controlled by genetic networks. By constructing synthetic versions of these genetic networks, we can program bacteria to behave in completely novel and useful ways. In this talk, we present our work on designing bacterial edge detectors: bacteria that use cell-cell communication and parallel computation to sense where borders between lighted and dark regions exist and report the edges. We use a mathematical model combining statistical mechanics with partial differential equations to determine which alterations to the synthetic gene network will improve the bacterial edge detection.


Work done in collaboration with J. Tabor and C.A. Voigt.

02:00 PM
03:00 PM
John Doyle - Robust design and control (robust yet fragile)

N/A

Name Affiliation
Aguda, Baltazar bdaguda@gmail.com Mathematical Biosciences Institute, The Ohio State University
Alves, Rui ralves@cmb.udl.cat ralves@cmb.udl.es Dept. Ciencies Mediques Basiques, Universitat de Lleida
Anders, Shilo Cognitive Systems Engineering Lab, The Ohio State University
Bell, Jonathan jbell@umbc.edu MBI - Long Term Visitor, The Ohio State University
Best, Janet jbest@mbi.osu.edu
Branlat, Mathieu Cognitive Systems Engineering Lab, The Ohio State University
Brynildsen, Mark mbrynild@ucla.edu Chemical and Biomolecular Engineering, University of California, Los Angeles
Coskun, Huseyin hcusckun@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Day, Judy jday@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Djordjevic, Marko mdjordjevic@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Doyle , John doyle@cds.caltech.edu Control & Dynamical Systems, California Institute of Technology
Enciso, German German_Enciso@hms.harvard.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Endy, Drew endy@mit.edu Biological Engineering Division, Massachusetts Institute of Technology
Fiehn, Oliver ofiehn@ucdavis.edu Genome Center And Bioinformatics Program, University of California, Davis
Gang, David gang@ag.arizona.edu Department of Plant Sciences, University of Arizona
Gold, Larry lgold@somalogic.com CEO & Chairman of the Board, SomaLogic, Inc.
Grajdeanu, Paula pgrajdeanu@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Green, Edward egreen@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Grotewold, Erich grotewold.1@osu.edu Plant Biology and Plant Biotechnology Center, The Ohio State University
Hawkins, Kristy kristy@caltech.edu Chemistry and Chemical Engineering, California Institute of Technology
Heinzle , Elmar e.heinzle@mx.uni-saarland.de Biochemical Engineering, Universit""at des Saarlandes
Huang, Kun khuang@bmi.osu.edu Biomedical Informatics, The Ohio State University
Hwa, Terence hwa@matisse.ucsd.edu Physics, University of California, San Diego
Igoshin, Oleg igoshin@rice.edu Department of Bioengineering, Rice University
Kao, Chiu-Yen kao.71@osu.edu MBI - Long Term Visitor, The Ohio State University
Keasling, Jay keasling@berkeley.edu Department of Chemical Engineering, University of California, Berkeley
Khammash, Mustafa khammash@engineering.ucsb.edu Dept. of Mechanical Engineering, University of California, Santa Barbara
Kim, Yangjin ykim@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Lou, Yuan lou@math.ohio-state.edu MBI - Long Term Visitor, The Ohio State University
Matzavinos, Tasos tasos@math.ohio-state.edu MBI - Long Term Visitor, The Ohio State University
Michener, Josh michener@caltech.edu California Institute of Technology
Morison, Alex morison.6@osu.edu Cognitive Systems Engineering Lab, The Ohio State University
Nevai, Andrew anevai@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Nielsen, David nielsend@MIT.EDU Chemical Engineering Department, Massachusetts Institute of Technology
O'Maille, Paul omaille@salk.edu The Salk Institute for Biological Studies, University of California, San Diego
Oster, Andrew aoester@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Patterson, Emily Cognitive Systems Engineering Lab, The Ohio State University
Prue, Brian Cognitive Systems Engineering Lab, The Ohio State University
Rempe, Michael mrempe@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Salis, Howard cavoigt@picasso.ucsf.edu University of California - San Francisco
Salvador, Armindo a.salvador@mail.telepac.pt Molecular Systems Biology Group, University of Coimbra
Schugart, Richard richard.schugart@wku.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Schwacke, John schwacke@musc.edu Biostatistics, Bioinformatics,&Epidemiology, Medical University of South Carolina
Shih, Chih-Wen shih@math.ohio-state.edu MBI - Long Term Visitor, The Ohio State University
Smith, Greg greg@as.wm.edu MBI - Long Term Visitor, The Ohio State University
Smolke, Christina smolke@cheme.caltech.edu Chemical Engineering, California Institute of Technology
Srinivasan, Partha p.srinivasan35@csuohio.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Stigler, Brandy bstigler@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Sun, Shuying ssun@mbi.osu.edu Mathematical Biosciences Institute (MBI), The Ohio State University
Szomolay, Barbara b.szomolay@imperial.ac.uk Mathematical Biosciences Institute (MBI), The Ohio State University
Tjaden, Brian btjaden@wellesley.edu Computer Science, Wellesley College
Valente, Andre andre.valente@biocant.pt Systems Biology Unit, Biocant
Voit, Eberhard eberhard.voit@bme.gatech.edu Dept. of Biomedical Engineering, Georgia Tech and Emory University
Voshell, Martin Cognitive Systems Engineering Lab, The Ohio State University
Win, Maung maung@caltech.edu Chemical Engineering, California Institute of Technology
Woods, David woods.2@osu.edu Cognitive Systems Engineering Lab, The Ohio State University
Workman, Chris chris.workman@gmail.com Center for Biological Sequence Analysis, Technical University of Denmark
Wreathall, John Cognitive Systems Engineering Lab, The Ohio State University
Xu, Ying xyn@bmb.uga.edu Biochemistry and Molecular Biology, University of Georgia
Zelik, Dan Cognitive Systems Engineering Lab, The Ohio State University
Zhang, Yali zhang.387@math.ohio-state.edu OSBP, The Ohio State University
Analysis of transcription networks in E. coli

N/A

Robust design and control (robust yet fragile)

N/A

Engineering biology (making some rules)

N/A

Stress Response Metabolism in Chlamydomonas reinhardtii

The green alga Chlamydomonas reinhardtii is a good biological model to study fundamental biological questions ranging from motility, photosynthesis to metabolism. As a photosynthetic cell, Chlamydomonas thrives in minimal media, carbon dioxide and light, but it can also grow heterotrophically on acetate. Unlike yeast or E.coli, any metabolite detected in the intra- or extracellular space is therefore a product of its enzymatic machinery, since only inorganic nutrients are needed. In addition, cell division of Chlamydomonas can be synchronized by light periods, so at any given time >95% of all cells in a culture are identical. Chlamydomonas batch cultures enable pursuing multiple experiments with limited efforts in small time frames, yet high analytical accuracy and precision.


We have utilized Chlamydomonas to study its metabolic responses under nitrogen depletion time courses using four different levels of N-supply and four different time points of stress response, each with eight independent replicates. Metabolite profiles were analyzed using improved methods that had median technical errors of 16% CV on 80 identified compounds using 5x10^6 cells per sample. Data were processed using an automated database approach and data were analyzed in a first pass using classic univariate and multivariate statistics. The dominant effect on metabolic variance was found to be depending on cell cycles states which are controlled by putrescine metabolism in this organism. In order to further the underlying understanding of additional metabolic perturbations by the physiological stress treatment, data were mean centered to the standard N-condition controls and analyzed for biochemical changes. Surprisingly, large and non-uniform differences could be observed for both dose and time of N-depletion on a wide number of metabolites. Data analysis was complemented by analyzing metabolic networks using Likelynet, a Bayesian likelihood method that is geared towards unbiased detection and verification of linear relationships in metabolic datasets, taking into account the technical error estimates for each variable (Kose F. et al, BMC Bioinformatics 2007, 8:162 http://www.biomedcentral.com/1471-2105/8/162 ).


Work done in collaboration with Do Yup Lee, Jan Budzcies, and Frank Kose.

Mutagenesis, knockout, and transfer of genes

N/A

The Plasma Proteome: An Integrator of Human Biochemical Systems Analysis?

Human biology functions through (largely unknown and) bewildering sets of interacting molecules. Even the parts list is incomplete - while we know that roughly 23,000 human proteins are expressed, each protein can be changed by alternative splicing, variable post-translational adducts, and protein processing. Perhaps the number of different human proteins is a couple hundred thousand, but no one really knows. The situation for RNA is worse - it is likely that in human cells every possible RNA is expressed at a low level, adding another 60,000,000 non-overlapping RNA 100-mers to the mix. If one loves the idea of a pre-biotic RNA World, one would be foolish to discount the possible functions available today in this deep human RNA sequence space. Bacteria have smaller genomes and parts lists, and have had more "genetics" - between faster generation times and higher mutation rates, bacteria are engineered today through a more complete Darwinian process than has been possible for mammals.


Thinking this way raises a question for metabolic engineering - should one design or evolve novel biochemical systems? Clunky mammals and less-clunky bacteria suggest that evolution strategies should be tried.


The deepest combinatorial selection paradigm (SELEX) has provided an astonishing set of reagents (aptamers), and at the same time provided lessons for how to manage evolution/selective strategies. Even though I'd like to lecture about the human plasma proteome (the main interest at SomaLogic), I will focus my talk on the possible value of in vitro and in vivo selections aimed at the discovery of novel biochemical systems.

Metabolic engineering: Where we are and what are the main issues today

Metabolic engineering has been defined as "the improvement of cellular activities by the manipulation of enzymatic, transport, and regulatory functions of the cell with the use of recombinant DNA technologies." The elucidation of the genome sequences for many microbes, fungi, animals and plants has provided a number of unique tools to tackle the challenge of engineering metabolic pathways as part of interdisciplinary efforts that integrate biology and chemistry with engineering and mathematics. However, fundamental issues remain, such as the adverse social reaction to the utilization of genetically modified organisms (GMO), the difficulties associated with predicting the effect of genetic manipulations on the metabolome, and the problems associated with targeting metabolites to the desired cellular or sub-cellular locations. These and other issues will be described, putting metabolic engineering within the perspective of alternatives (such as chemical synthesis) with the goal to provide a perspective of the opportunities and future of the field for interdisciplinary interactions.



 
Fluxes - quantifying flows in metabolic pathways

In a first part of my talk I will briefly review relevant methods for metabolic flux analysis. This includes metabolite balancing and flux analysis using labelled substrates with necessary experimental and computational methods. I will finish my talk with two recent case studies:



  1. Metabolic fluxes of a plant secondary metabolite pathway. In this case study dynamic labelling experiments were used to elucidate pathway fluxes in native potatoe and after addition of an elucidator.

  2. Regulation of central metabolic fluxes in Bacillus subtilis. Fluxes in various mutants of B. subtilis using different substrate combinations were determined after model based experimental planning.

Quantitative characteristics of gene regulation by small RNA

An increasing number of small RNAs (sRNA) have been shown to regulate critical pathways in prokaryotes and eukaryotes. In bacteria, sRNA regulation is predominantly involved in coordinating intricate stress responses. The mechanisms by which sRNA modulate expression of its targets are diverse. In common to most is the possibility that the level of functional sRNA may be altered via its interaction with its targets. Aiming to understand the unique role played by sRNAs, we study quantitatively two classes of bacterial sRNAs in Escherichia coli using a combination of experimental and theoretical approaches. Our results demonstrate that sRNA provides a novel mode of gene regulation, with characteristics distinct from those of protein-mediated gene regulation. These include a threshold-linear response with a tuneable threshold, a robust noise resistance characteristic, and a built-in capability for hierarchical cross talk. Knowledge of these special features of sRNA-mediated regulation is crucial towards understanding the subtle functions that sRNAs play in coordinating various stress-relief pathways, and can help guide the design of synthetic genetic circuits with properties difficult to attain with protein regulators alone.



 
Stochastic gene expression

The cellular environment is abuzz with noise. Generated by random molecular events, cellular noise not only results in random fluctuations within individual cells but it is also a source of phenotypic variability among clonal cellular populations. In some instances fluctuations are suppressed downstream through an intricate dynamical network that acts to filter the noise. Yet in other instances, noise induced fluctuations are exploited to the cell's advantage. Intriguing mechanisms that rely on noise include stochastic switches, coherence resonance in oscillators, and stochastic focusing. While mathematical models of genetic networks often represents gene expression and regulation as deterministic processes with continuous variables, the stochastic nature of cellular noise necessitates an approach that models these variables as discrete and stochastic. In this framework, probability densities of the system states evolve according to a (usually infinite dimensional) Chemical Master Equation (CME). Until recently, sample trajectories have been computed almost exclusively with Kinetic Monte Carlo methods, such as Gillespie's Stochastic Simulation Algorithm. In this talk we present a new direct approach for computing the relevant statistics, which involves the projection of the solution of the CME onto finite subsets. We illustrate the algorithm underlying our Finite State Projection approach and introduce a variety of systems theory based modifications and enhancements that enable large reductions and increased efficiency with little to no loss in accuracy. Model reduction techniques based on linear perturbation theory allow for the systematic projection of multiple time scale dynamics onto a slowly varying manifold of much smaller dimension. The proposed projection approach is illustrated on few important models of genetic regulatory networks.

Basic science driving protein engineering: Questions shape the tools

Advances in structural and molecular biology have spurred the proliferation of protein engineering technologies, allowing fundamental questions about protein evolution to become approachable. The questions themselves, in turn, can be the drivers for the development of new tools. I will describe how my interests in protein evolution shaped the development of structure-based combinatorial protein engineering (SCOPE), a tool for connecting evolutionary endpoints in local and global sequence space. I will first discuss the inception of SCOPE as a homology-independent recombination method and its application to create multiple-crossover libraries from distantly-related DNA polymerases, and then describe adapting the technique for combinatorial mutagenesis to recapitulate the more recent functional divergence of closely-related terpene cyclases. Throughout my discussions, I will highlight probabilistic considerations of library design, detail key experimental results and what they tell us about the evolution of specialized metabolism and implications for metabolic engineering.



 
System design (assembling novel systems)

Bacteria are tiny chemical factories that can sense their environment and react to stimuli according to a pre-programmed response. These responses are controlled by genetic networks. By constructing synthetic versions of these genetic networks, we can program bacteria to behave in completely novel and useful ways. In this talk, we present our work on designing bacterial edge detectors: bacteria that use cell-cell communication and parallel computation to sense where borders between lighted and dark regions exist and report the edges. We use a mathematical model combining statistical mechanics with partial differential equations to determine which alterations to the synthetic gene network will improve the bacterial edge detection.


Work done in collaboration with J. Tabor and C.A. Voigt.

Design principles of moiety supply units in metabolic networks

Metabolic networks have a bow-tie architecture: a wide diversity of nutrients is disassembled into a few molecular currencies, which are then reassembled into a large variety of other molecules. At the "knot" of this bowtie lie cycles whereby a set of reactions transfer a molecular group (moiety) from various donor metabolites to a common carrier (metabolic currency) from which another set of reactions transfers the moiety to various accepting metabolites. These circuits couple moiety supply to demand. Their role, and the performance criteria they should fulfill, are akin to those of a power-supply unit in an electronic circuit.


The first part of this talk will explore the design principles enabling this general class of metabolic circuits to operate effectively as "moiety-supply units". The second part of the talk will examine quantitative aspects of the design of concrete biological realizations of these circuits.

A framework for programming integrated RNA devices

Recent progress in developing frameworks for the construction of RNA devices is enabling rapid advances in cellular engineering applications. These devices provide scalable platforms for the construction of molecular communication and control systems for reporting on, responding to, and controlling intracellular components in living systems. Research that has demonstrated the modularity, portability, and specificity inherent in these molecules for cellular control will be highlighted and its implications for synthetic and systems biology research will be discussed. In addition, new tools that translate sequence information to device function to enable the forward design and optimization of new devices will be discussed. The flexibility of the specified framework enables these molecules to be integrated as systems that perform higher-level signal processing based on molecular computation strategies. The application of these molecular devices to studying cellular systems through non-invasive /in vivo /monitoring of biomolecule levels and to regulating cellular behavior, in particular in the control and optimization of biosynthesis, will be discussed.



 
Characterizing noncoding RNA genes in bacteria

Small noncoding RNAs are genes for which RNA, rather than protein is the functional end product. In bacteria, many small RNA genes (sRNAs) appear to act as post-transcriptional regulators by basepairing with target messenger RNAs. In this talk, we will look at computational and experimental approaches to characterize these sRNA genes in bacteria. First, we will describe high-throughput approaches for identifying sRNA genes in a bacterial genome. In particular, we will consider a probabilistic model that combines heterogeneous data sources (including primary sequence data, comparative genomics information, and microarray expression data) for the purpose of predicting sRNA genes throughout a genome. We will then investigate methods, both computational and experimental, for characterizing regulatory targets of sRNA action. Finally, we will explore how these high-throughput approaches are used to elucidate the roles of specific sRNA genes and the pathways in which the genes are involved.



 
Estimation of Metabolic Model Parameters from Time Series Data

Stoichiometric approaches have been tremendously successful as mathematical models in metabolic engineering. Their linearity permits an unparalleled repertoire of mathematical and computational tools, and the combination of stoichiometric models with experimental data has yielded valuable insights into flux distributions under different conditions. However, as we strive to understand the details of control and regulation in vivo at a deeper level, refined models are needed, and these must be nonlinear. While simulations with nonlinear metabolic models are no longer a significant computational hurdle, the estimation of suitable parameter values continues to be a major challenge. In this presentation I will review current approaches to metabolic parameter estimation, especially for time series data, and demonstrate why it is important to obtain fast solutions on standard computers. As an example for many aspects of my presentation, I will use the regulation of glucose utilization in Lactococcus lactis, for which we have high-precision in vivo data describing the dynamics of intracellular metabolite pools.



  1. Voit, E.O.: Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists, xii + 530 pp., Cambridge University Press, Cambridge, U.K., 2000.

  2. Voit, E.O., J.S. Almeida, S. Marino, R. Lall, G. Goel, A.R. Neves, and H. Santos. Regulation of Glycolysis in Lactococcus lactis: An Unfinished Systems Biological Case Study. IEE Proc. Systems Biol. 153, 286-298, 2006.

  3. Voit, E.O., A.R. Neves, and H. Santos. The Intricate Side of Systems Biology. PNAS, 103(25), 9452-9457, 2006.

  4. Goel, G., I-Chun Chou, and E.O. Voit: Biological Systems Modeling and Analysis: A Biomolecular Technique of the 21st Century. J. Biomolec. Techn. 17, 252-269, 2006.

Gene circuitry (inferring natural circuits in bacteria)

N/A