Flux balance analysis in metabolic networks

Transcription

1 Flux balance analysis in metabolic networks Lecture notes by Eran Eden, additional slides by B. Palsson from and Jordan Nikkel Edit by M.Reczko

2 Systems biology

3 Flux balance analysis in metabolic networks. 1.Metabolic networks Metabolism is the process involved in the maintenance of life. It is comprised of a vast repertoire of enzymatic reactions and transport processes used to convert thousands of organic compounds into the various molecules necessary to support cellular life Kenneth et al Flux Balance Analysis pes.jpg&imgrefurl= bnid=1c0069trx90j:&tbnh=142&t bnw=150&hl=en&start=1&prev=/images%3fq%3d%2bpipes%2b%26hl%3den%26lr%3d

4 Lecture plan I) Creating an in silico model in order to describe an organisms metabolism in steady state II) Connecting in silico Segre` model et al. (2002) to in-vivo experiments in e. coli Schilling et al. (2000)

5 Motivation for studying metabolic pathways Better understanding of cellular physiology. Understanding vulnerabilities of unicellular metabolism. Etc

6 Lets begin constructing the model Step (I) - Definitions We begin with a very simple imaginary metabolic network represented as a directed graph: Vertex - substrate/metabolite concentration. Edge - flux (conversion mediated by enzymes of one substrate into the other) Internal flux edge How do we define a biologically significant system boundary? External flux edge

7 (II) - Dynamic mass balance Concentration vector dx dt Stoichiometry Matrix S v Flux vector

8 (II) - Dynamic mass balance Concentration vector dx dt Stoichiometry Matrix Solution! S v Flux vector Problem V=V(k1, k2,k3 ) is actually a function of concentration as well as several kinetic parameters. it is very difficult determine kinetic parameters experimentally. Consequently there is not enough kinetic information in the literature to construct the model. In order to identify invariant characteristics of the network we assume the network is at steady state.

9 (III) - Dynamic mass balance at steady state 1. What does steady state mean? 2. Is it biologically justifiable to assume it? 3. Does it limit the predictive power of our model? The steady state approximation is generally valid because of fast equilibration of metabolite concentrations (seconds) with respect to the time scale of genetic regulation (minutes) Segre 2002 Yes 4. Most important question

10 4. Why does the steady state assumption help us solve our problem? dx S v dt 0 Sv Steady state assumption 0 0 0

11 (VI) adding constraints Constraints on internal fluxes: Constraints on external fluxes: Source Sink bj 0 b 0 Sink/source j b j vi is unconstrained 0, i In other words flux going into the system is considered negative while flux leaving the system is considered positive. Remark: later on we will impose further constraints both on the internal flux as well as the external flux

12 (V) Flux cone and metabolic capabilities 0 Sv Observation: the number of reactions considerably exceeds the number of metabolites The S matrix will have more columns than rows The null space of viable solutions to our linear set of equations contains an infinite number of solutions. What about the constraints? C The solution space for any system of linear homogeneous equations and inequalities is a convex polyhedral cone. - Schilling 2000 Our flux cone contains all the points of the null space with non negative coordinates (besides exchange fluxes that are constrained to be negative or unconstrained)

13 (V) Flux cone and metabolic capabilities What is the significance of the flux cone? It defines what the network can do and cannot do! Each point in this cone represents a flux distribution in which the system can operate at steady state. The answers to the following questions (and many more) are found within this cone: what are the building blocks that the network can manufacture? how efficient is energy conversion? Where is the critical links in the system?

14 (VI) Navigating through the flux cone using Extreme pathways Next thing to do is develop a way to describe and interpret any location within this space. We will not use the traditional reaction/enzyme based perspective Instead we use a pathway perspective: Extreme rays - extreme rays correspond to edges of the cone. They are said to generate the cone and cannot be decomposed into nontrivial combinations of any other vector in the cone. - schilling 2000 What is the analogy in linear algebra? We use the term Extreme Pathways when referring to Extreme rays of a convex polyhedral cone that represents metabolic fluxes Differences Unlike a basis the set of, extreme pathways is typically unique Any flux in the cone can be described using a non negative combination of extreme rays.

15 (VI) Navigating through the flux cone using Extreme pathways Extreme Pathways will be denoted by vector EP i (0 i k) Every point within the cone can be written as a non-negative linear combination of the extreme pathways. C v k C { v v w EP w 0 i} i i, i i1 Pw v In biological context this means that : any steady state flux distribution can be represented by a non-negative linear combination of extreme pathways.

16 The entire process from a bird s eyes Compute steady state flux Convex + constraints Identify Extreme pathways (using algorithm presented in Schilling 2000)

17 Lets look at a specific vector v : Example v Is v inside the flux cone? Easy to check 1. Does v fulfill constraints? 2. Is v in the null space of Sv=0?

18 (I) Narrowing the steady state flux cone The steady state flux cone contains an infinite flux distributions! Only a small portion of them is physiologically feasible. More constraints on the external fluxes. These depend on factors as: Organism Environment and accessibility substrates maximum rates of diffusion mediated transport Etc

19 (II) Calculating optimal flux distribution The constrained flux cone in E.coli contains ~10^6 (Schilling 2001) How can we identify a biologically meaningful flux? Assumption the metabolic network is optimized with respect to a certain objective function Z. Z will be a linear function. Later, we will deal with how exactly to choose Z

20 What we want to do is find the vector v in the flux cone which maximizes Z. this can be can formulated as an optimization problem: j j j Sv0 Minimize/Maximize Z c v S.T + inequality constraints This optimization problem is a classical linear programming (LP) problem that can be solved using the simplex algorithm. W. Wiechert. Journal of biotechnology(2002)

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32 (III) How to choose the objective function Z We want to choose a Z that is biologically meaningful. Reasonable options could be: 1. Z: Cellular growth (maximization) 2. Z: Particular metabolite engineering (maximization) 3. Z: Energy consumption (minimization) Example: cellular growth is correlated with the production of B,D and 2E. We want a v that: (A) Resides in side the cone. (B) maximizes Z=B+D+2E.

33 (III) How to choose the objective function Z 1. It has been shown that under rich growth conditions (i.e. no lack of phosphate and nitrogen), E. Coli grows in a stoichiometrically optimal manner. (Schilling 2001, Edwards 1994) 2. It is reasonable to hypothesize that unicellular organisms have evolved toward maximal growth performance. (Segre, 2002.) We shall use Z which reflects: Cellular Growth

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35 Our study: The benefit of cooperation

36 Mutual environment Biomass 1 1 B t B1 t B2 t B0e B0e 2 2 1tt 2 Biomass When none of the different populations can grow further in the shaped medium the total biomass that has been accumulated is compared to the final biomass that the homogeneous population has produced given the same initial environment. t t

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38 OptFlux demo wget chmod u+x OptFlux linux-x64-installer.run./OptFlux linux-x64-installer.run

39 Load model

40 Browse model

41 Simulate model

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43 Create conditions

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45 Simulate modified model

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47 Find optimized strain

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50 Growth rate (IV) Phenotype phase planes- PPP Predicting cellular growth Schilling 2001 X axis Succinate uptake rate Y axis Oxygene uptake rate Z axis - Growth rate (maximal value of the objective function as function of succinate and oxygen uptake) Observations: Metabolic network is unable to utilize succinate as sole carbon source in anaerobic conditinos. Region 1: oxygen excess this region is wasteful (less carbon is available for biomass production since it is oxidized to eliminate the excess oxygen.) Line of optimality

51 Growth rate (IV) Phenotype phase planes- PPP Predicting cellular growth Schilling 2001 X axis Succinate uptake rate Y axis Oxygene uptake rate Z axis - Growth rate (maximal value of the objective function as function of succinate and oxygen uptake) Observations: Region 2 limitation on both oxygen and succinate Region 3- the uptake of additional succinate has a negative effect. Cellular resources are required to eliminate excessive succinate.

52 (IV) Phenotype phase planes- PPP Predicting cellular growth The EPs can be projected onto the plane. Eps are used to explain the different regions from a pathway perspective PPPs were also constucted for Malate/oxygen and Glucose/oxygen

53 Model vs. biological experiments

54 Does E. coli behave according to optimal behavior predictions? E. coli was grown with malate as sole carbon source. A range of substrate concentrations and temperatures were used in order to vary the malate uptake rate (MUR). Oxygen uptake rate (OUR) and growth rate were measured...

55 Does E. coli behave according to optimal behavior predictions? Malate/oxygen PPP 1- The experimentally determined growth rate were on the line of optimality of the PPP! Ibarra et al., Nature 2002

56 Does E. coli behave according to optimal behavior predictions? Is the optimal performance on malate stable over prolonged periods of time? Evolution of E. coli on malate was studied for 500 generations in a single condition Malate/oxygen PPP 2- An adaptive evolution was observed with an increase of 19%in growth rate! 3- Same adaptive evolution was observed for succinate and Malate! Ibarra et al., Nature 2002

57 Does E. coli behave according to optimal behavior predictions? Same experiments were made using glycerol as sole carbon source Day 0 Sub optimal growth Why? Day 1-40 evolution toward optimal growth Day 40 optimal growth Day 60 optimal growth (no change)

58 Considering instances where FBA predictions are inaccurate MOMA What happens to the metabolism in the case of a mutation/genetically engineered bacteria? What happens in terms of the flux cone? 0 Sv

59 Knockout to pyruvate kinase Considering instances where FBA predictions are inaccurate FBA assumes optimality of growth for wild type This assumption is not necessarily correct some instances

60 Considering instances where FBA predictions are inaccurate MOMA Is there any other objective function Z that can capture the biological essence of these mutations? Perhaps another model MOMA - minimization of metabolic adjustments [Segre, Vituk and Church 2002]

61 MOMA Uses the same steady state flux cone as FBA. Relaxes the assumption of maximal optimal growth. a mutant is likely to display a suboptimal flux distribution between wild-type optimum and mutant optimum. Mutant optimal growth FBA Mutant growth actual MOMA Wild-type growth FBA

62 How does MOMA work? Assumption Initially, the mutant remains as close as possible to the wildtype optimum in terms of flux values. Mutant optimal growth FBA Mutant growth actual MOMA Wild-type growth FBA

63 How does MOMA work? In other words: MOMA searches for the flux distribution in the mutant flux space which is closest to the optimal flux distribution in the wild-type flux space. Optimal growth wild type Mutant growth actual

64 How does MOMA work? Formally: V wt the wild-type optimal growth vector. V m a vector in mutant flux space. Find V m which minimizes the Euclidian distance to V wt : m wt m wt D( V, V ) ( V V ) n i1 i i 2 This can be stated as a QP problem. That is, minimize 1 T f ( x) L x x Qx 2 Under a set of linear constraints.

65 Comparing MOMA and FBA on mutant strains

66 Conclusions selection pressure results in optimal performance through evolutionary process. This optimal performance can be predicted using in-silico modeling. Unicellular evolution can be thought in terms of an iterative optimization procedure whose objective function maximizes the organisms ability to survive and proliferate. If given enough time (iterations) a local maxima is struck.

67 Bibliography [1] Daniel Segre`, Dennis Vitkup, and George M. Church. Analysis of optimality in natural and perturbed metabolic networks. PNAS, vol. 99, [2] C. H. Schilling, D. Letscher and Bernhard Palsson. Theory for the Systemic Definition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective. J. theor. Biol. (2000) [3] Schillling et. Al Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnology and bioengineering, [4] Edwards et al Characterizing the metabolic phenotype A phenotype phase plan. Biotechnology and bioengineering [5] Kenethh et al. Advances in flux balance analysis. Current Opinion in Biotechnology. [6] Ibarra et al. Escherichia coli k-12 undergoes adaptive evolution to achiev in silico predicted optimal growth. Nature [7] W. Wiechert. Modeling and simulation: tools for metabolic engineering. Journal of biotechnology(2002) [8] Cornish-Bowden. From genome to cellular phenotype- a role for meatbolic flux analysis? Nature biotechnology, vol 18, [9] Schuster et al. Detection of elelmtary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. TIBTECH 1999 [10] J. Papin, Nathan D Price, B. Palsson. Extreme pathway lengths and reaction participation in genome scale metabolic networks. Genome research, [11] Stelling eta l. Metabolic netwrok structure determines key aspects of functionality and regulation. Nature [12] A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.

68 Thanks

69 Constructing a model things to consider 1. Dynamic nature of biological networks. So far in the seminar we have focused approaches that analyze the topology of biological networks. Therefore we will try to base our model on network characteristics that remain invariant.

70 Constructing a model things to consider 2. Abstraction Resolution: How much do we get into details? What building blocks do we use to describe the network? High resolution (A) Metabolites and enzymes (B) Pathways Low (C) special pathways resolution

71 A model of metabolism from a pathway perspective Bernhard Ø. Palsson Elementary flux modes Schuster et al Schuster et al Jorg Stelling et al Extreme pathways Schilling et al Ibarra et al Papin et al Schilling et al. 2001

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82 We can reformulate this concept using matrix notation:, 1 ' 0 k i i i i v w EP w can be v be represented using a nonnegative linear combination of extreme pathways? The vector w gives us a pathway based perspective of the network functioning! Example continuation = w w w w w w w v v v v v v v b b b b = v =4*p1+1*p2+1*p3 w v P w w w w w w w

83 We learnt that EPs are a subset containing special pathways whose selection from the entire set of pathways was mathematically inspired - but do EPs really assist in metabolism analysis? Is the steady state model consistent with real biological metabolic networks? The proof of the pudding is in the tasting Next lecture: we will examine the value of the steady state according to its ability to predicting an organism s characteristics

84 Bibliography [1] Daniel Segre`, Dennis Vitkup, and George M. Church. Analysis of optimality in natural and perturbed metabolic networks. PNAS, vol. 99, [2] C. H. Schilling, D. Letscher and Bernhard Palsson. Theory for the Systemic Definition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective. J. theor. Biol. (2000) [3] Schillling et. Al Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnology and bioengineering, [4] Edwards et al Characterizing the metabolic phenotype A phenotype phase plan. Biotechnology and bioengineering [5] Kenethh et al. Advances in flux balance analysis. Current Opinion in Biotechnology. [6] Ibarra et al. Escherichia coli k-12 undergoes adaptive evolution to achiev in silico predicted optimal growth. Nature [7] W. Wiechert. Modeling and simulation: tools for metabolic engineering. Journal of biotechnology(2002) [8] Cornish-Bowden. From genome to cellular phenotype- a role for meatbolic flux analysis? Nature biotechnology, vol 18, [9] Schuster et al. Detection of elelmtary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. TIBTECH 1999 [10] J. Papin, Nathan D Price, B. Palsson. Extreme pathway lengths and reaction participation in genome scale metabolic networks. Genome research, [11] Stelling eta l. Metabolic netwrok structure determines key aspects of functionality and regulation. Nature [12] A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.

85 Lecture #2 - Flux balance analysis in metabolic networks Lecture notes by Eran Eden

86 Today s topic: Assessing the relevance of the steady state flux balance analysis model to real biological questions or What has evolution got to do with optimization theory?

87 Predicting the E.coli optimal growth Ibarra et al. Escherichia coli k-12 undergoes adaptive evolution to achiev in silico predicted optimal growth. Nature Daniel Segre`, Dennis Vitkup, and George M. Church. Analysis of optimality in natural and perturbed metabolic networks. PNAS, vol. 99, Edwards et al. Characterizing the metabolic phenotype. A phenotype phase plan. Biotechnology and bioengineering Kenethh et al. Advances in flux balance analysis. Current Opinion in Biotechnology Schillling et. Al Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnology and bioengineering, 2001.

88 Last lecture - a short reminder Our objective was to construct a metabolic network model from a pathway perspective

89 Last lecture - a short reminder dx Steady state S v dt 0 Sv constraints

90 Last lecture - a short reminder What is the biological interpretation of any point in the flux cone?