5/10/2015Yang Yang, Candidacy Seminar1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana University,

Slides:



Advertisements
Similar presentations
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
Advertisements

Bioinformatics 3 V18 – Kinetic Motifs Mon, Jan 12, 2015.
Signal transduction in bacterial chemotaxis Lengeler et al. pp
Sept 25 Biochemical Networks Chemotaxis and Motility in E. coli Examples of Biochemical and Genetic Networks Background Chemotaxis- signal transduction.
Marcus Tindall Centre for Mathematical Biology Mathematical Institute St Giles’ Oxford. PESB, Manchester, 2007.
Chp 2 Molecules and Cells in Animal Physiology Read Chp 2 of the book Use the notes for Human Physiology We will see metabolism and the enzymes in more.
Extracting Essential Features of Biological Networks Natalie Arkus, Michael P. Brenner School of Engineering and Applied Sciences Harvard University.
Computational Biology, Part 17 Biochemical Kinetics I Robert F. Murphy Copyright  1996, All rights reserved.
Chemotaxis: Another go Chrisantha Fernando Systems Biology Centre Birmingham University Chrisantha Fernando Systems Biology Centre Birmingham University.
Modeling the chemosensing system of E. coli
2 component regulatory systems Maltose=effector, BUT if signal not DIRECTLY involved, but needs to be transmitted and changed = signal transduction Sensor.
Bacterial Chemotaxis Dr. Chrisantha Fernando Systems Biology Centre University of Birmingham, UK March 2007 Dr. Chrisantha Fernando Systems Biology Centre.
Systems Biology Ophelia Venturelli CS374 December 6, 2005.
Molecular Physiology: Enzymes and Cell Signaling.
Chapter 8 Applications In physics In biology In chemistry In engineering In political sciences In social sciences In business.
BIOC3800 Sensory Transducers Dr. J.A. Illingworth.
Metabolic pathway alteration, regulation and control (5) -- Simulation of metabolic network Xi Wang 02/07/2013 Spring 2013 BsysE 595 Biosystems Engineering.
E. coli exhibits an important behavioral response known as chemotaxis - motion toward desirable chemicals (usually nutrients) and away from harmful ones.
Bacterial chemotaxis lecture 2 Manipulation & Modeling Genetic manipulation of the system to test the robustness model Explaining Ultrasensitivity and.
1 Introduction to Biological Modeling Steve Andrews Brent lab, Basic Sciences Division, FHCRC Lecture 1: Introduction Sept. 22, 2010.
Computational biology of cancer cell pathways Modelling of cancer cell function and response to therapy.
Response: Cell signaling leads to regulation of transcription or cytoplasmic activities Chapter 11.4.
The chemotaxis network is able to extract once the input signal varies slower relative to the response time of the chemotaxis network. Under an input signal.
Overview of next five lectures: How is directional motility accomplished at the single cell level? An emphasis on experimental approaches for testing models.
Adaptation Essential for sensory perception Nearly universal Salmonella How to move towards unseen food?
Robustness in protein circuits: adaptation in bacterial chemotaxis 1 Information in Biology 2008 Oren Shoval.
Picture of an enzymatic reaction. Velocity =  P/  t or -  S/  t Product Time.
March 8, 2007March APS Meeting, Denver, CO1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana.
Cell Communication Chapter Cell Communication: An Overview  Cells communicate with one another through Direct channels of communication Specific.
Optimal Strategy in E. coli Chemotaxis: An Information Theoretic Approach Lin Wang and Sima Setayeshgar Department of Physics, Indiana University, Bloomington,
Fig 7.1. Fig 7.2 The bacteria flagella motor [source: Berg HC, Ann. Rev. Biochem 2003] Fig 7.3.
Hybrid Functional Petri Net model of the Canonical Wnt Pathway Koh Yeow Nam, Geoffrey.
L17. Robustness in bacterial chemotaxis response
Introduction to biological molecular networks
12/24/2015Yang Yang, Candidacy Seminar1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana University,
1/2/2016Yang Yang, Candidacy Seminar1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana University,
Optimal Strategy in E. coli Chemotaxis: An Information Theoretic Approach Lin Wang and Sima Setayeshgar Department of Physics, Indiana University, Bloomington,
Microbiology and Molecular Biology for Engineers IGEM, 20 June 2006.
Lin Wang Advisor: Sima Setayeshgar. Motivation: Information Processing in Biological Systems Chemical signaling cascade is the most fundamental information.
Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae Vu, T. T.,
Chap 18 The Genetics of Viruses and Bacteria. Structure of Virus Approximately 20 nm in diameter Their genome can contain DNA or RNA. Enclosed by a.
In-silico Implementation of Bacterial Chemotaxis Lin Wang Advisor: Sima Setayeshgar.
Approach…  START with a fine-tuned model of chemotaxis network that:  reproduces key features of experiments (adaptation times to small and large ramps,
The chemotaxis network is able to extract as much as information possible once the input signal varies slower relative to the response time of the chemotaxis.
BCB 570 Spring Signal Transduction Julie Dickerson Electrical and Computer Engineering.
Revised curriculum (1) December 16 (Tuesday) Second messengers
Adaptation as a nonequilibrium paradigm
Advanced Aspects of Chemotactic Mechanisms:
Near-Perfect Adaptation in Bacterial Chemotaxis
Bacterial Chemotaxis Bacteria swim toward attractants and away from repellents. Their motion is a biased random walk due to control of tumbling frequency.
Module 2: Robustness in Biochemical Signaling Networks
Xianfeng Song Lin Wang Sima Setayeshgar
Bacterial Chemotaxis Bacteria swim toward attractants and away from repellents. Their motion is a biased random walk due to control of tumbling frequency.
Masahiro Ueda, Tatsuo Shibata  Biophysical Journal 
1 Department of Engineering, 2 Department of Mathematics,
Near-Perfect Adaptation in Bacterial Chemotaxis
1 Department of Engineering, 2 Department of Mathematics,
1 Department of Engineering, 2 Department of Mathematics,
Near-Perfect Adaptation in Bacterial Chemotaxis
Yang Yang & Sima Setayeshgar
Mechanism of chemotaxis in E
Bacterial chemotaxis: The five sensors of a bacterium
Mathematical Models of Protein Kinase Signal Transduction
Near-Perfect Adaptation in Bacterial Chemotaxis
Toshinori Namba, Masatoshi Nishikawa, Tatsuo Shibata 
Near-Perfect Adaptation in Bacterial Chemotaxis
Simulating cell biology
Robustness of Cellular Functions
Presentation transcript:

5/10/2015Yang Yang, Candidacy Seminar1 Near-Perfect Adaptation in Bacterial Chemotaxis Yang Yang and Sima Setayeshgar Department of Physics Indiana University, Bloomington, IN

Bacterial Chemotaxis “Hydrogen atom” of biochemical signal transduction networks Paradigm for two-component receptor-regulated phosphorylation pathways Accessible for study by structural, biochemical and genetic approaches The chemosensory pathway in bacterial chemotaxis and propulsion system it regulates have provided an ideal system for probing the physical principles governing complex cellular signaling and response.

E. Coli as a Model Organism 5/10/2015Yang Yang, Candidacy Seminar3 Workhorse of molecular biology: Most studied cell in all science: … Small genome(~4300 genes). … Normal lack of pathogenicity … Ease of growth in lab  Basis of recent developements in biotechnology and genetic engineering, including living factory for producing human medicines  Basis for understanding of fundamental cellular processes: … cellular sensory systems, … regulation of gene expression, … cell division, etc.  Size: … 0.5 μm in diameter, … 1.5 μm in length  Cell cycle: … ~ 1 hour

Chemotaxis in E. coli 5/10/2015Yang Yang, Candidacy Seminar4 Dimensions: Body size: 1 μm in length 0.4 μm in radius Flagellum: 10 μm long Physical constants: Cell speed: μm/sec Mean run time: 1 sec Mean tumble time: 0.1 sec (Courtesy of Howard Berg group)

E. coli in Motion 5/10/2015Yang Yang, Candidacy Seminar5 From Berg & Brown, Nature (1972).

E. coli Flagellar Motor 5/10/2015Yang Yang, Candidacy Seminar6 From R. M. Berry, Encyclopedia of Life Science (2001). From P. Cluzel, et al., Science (2000).

Chemotaxis Signal Transduction Network in E. coli 5/10/2015Yang Yang, Candidacy Seminar 7 Histidine kinase Methylesterase Couples CheA to MCPs Response regulator Methyltransferase Dephosphorylates CheY-P CheB CheA CheW CheZ CheR CheY Signal Transduction Pathway Motor Response [CheY-P] Stimulus Flagellar Bundling Motion Run Tumble

Response to Step Stimulus 5/10/2015Yang Yang, Candidacy Seminar8 Fast responseSlow adaptation From Block et al., Cell (1982). From Sourjik et al., PNAS (2002).

Excitation and Adaptation 5/10/2015Yang Yang, Candidacy Seminar9

Precision of Adaptation 5/10/2015Yang Yang, Candidacy Seminar10 From Alon et al. Nature (1999). Precision of adaptation = steady state tumbling frequency of unstimulated cells / steady state tumbling frequency of stimulated cells Squares: Unstimulated cells Circles: Cells stimulated at t=0 (Each point represents data from 10s motion of cells.)

Robustness of Perfect Adaptation 5/10/2015Yang Yang, Candidacy Seminar11 From Alon et al. Nature (1999). Precision of adaptation robust to 50-fold change in CheR expression … …while … Adaptation time and steady state tumbling frequency vary significantly. Robustness of perfect adaptation

Robust Perfect Adaptation 5/10/2015Yang Yang, Candidacy Seminar Fast responseSlow adaptation From Sourjik et al., PNAS (2002). FRET signal [CheY-P] From Alon et al., Nature (1999). CheR fold expression Adaptation Precison Steady state [CheY-P] / running bias independent of value constant external stimulus (adaptation) Precision of adaptation insensitive to changes in network parameters (robustness) 12

This Work: Outline 5/10/2015Yang Yang, Candidacy Seminar13  New computational scheme for determining conditions and numerical ranges for parameters allowing robust (near-)perfect adaptation in the E. coli chemotaxis network  Comparison of results with previous works  Extension to other modified chemotaxis networks, with additional protein components  Conclusions and future work

E. coli Chemotaxis Signaling Network 5/10/2015Yang Yang, Candidacy Seminar14  Ligand binding  Methylation  Phosphorylation phosphorylation methylation Ligand binding E=F(free form), R(coupling with CheR), B(coupling with CheB p ) E’=F(free form), R(coupling with CheR)  =o(ligand occupied), v(ligand vacuum)  =u(unphosphorylated), p(phosphorylated)

Enzymatic reaction: Where E is the enzyme, S is the substrate, P is the product. A key assumption in this derivation is the quasi steady state approximation, namely that the concentration of the substrate-bound enzyme changes much more slowly than those of the product and substrate. Therefore, it may be assumed that it is in steady state: Michaelis-Menten Kinetics 5/10/2015Yang Yang, Candidacy Seminar15 where K m is the Michaelis Menten Constant (MM constant)

Reaction Rates 5/10/2015Yang Yang, Candidacy Seminar16

Approach … 5/10/2015Yang Yang, Candidacy Seminar17  START with a fine-tuned model of chemotaxis network that:  reproduces key features of experiments  is NOT robust  AUGMENT the model explicitly with the requirements that:  steady state value of CheY-P  values of reaction rate constants, are independent of the external stimulus, s, thereby explicitly incorporating perfect adaptation. : state variables : reaction kinetics : reaction rates : external stimulus

The steady state concentration of proteins in the network satisfy: The steady state concentration of = [CheY-P] must be independent of stimulus, s: where parameter allows for “near- perfect” adaptation. Reaction rates are constant and must also be independent of stimulus, s: Augmented System 5/10/2015Yang Yang, Candidacy Seminar18 Discretize s in range {s low, s high }

Physical Interpretation of Parameter, : Near-Perfect Adaptation 5/10/2015Yang Yang, Candidacy Seminar19  Measurement of c = [CheY-P] by flagellar motor constrained by diffusive noise Relative accuracy*,  Signaling pathway required to adapt “nearly” perfectly, to within this lower bound (*) Berg & Purcell, Biophys. J. (1977). : diffusion constant (~ 3 µM) : linear dimension of motor C-ring (~ 45 nm) : CheY-P concentration (at steady state ~ 3 µM) : measurement time (run duration ~ 1 second)

 Use Newton-Raphson (root finding algorithm with back-tracking), to solve for the steady state of augmented system,  Use Dsode (stiff ODE solver), to verify time- dependent behavior for different ranges of external stimulus by solving: Implementation 5/10/2015Yang Yang, Candidacy Seminar20

Converting from Guess to Solution 5/10/2015Yang Yang, Candidacy Seminar21 A B Starting from initial guess A, the solution to B is generated. T 3 autophosphorylation rate (k 3a ) Inverse of T 3 MM constant (K 3R -1 ) ● 3%<  <5% ● 1%<  <3% ● 0%<  <1%

Parameter Surfaces 5/10/2015Yang Yang, Candidacy Seminar22 ● 1%<  <3% ● 0%<  <1% Surface 2D projections Inverse of T 1 methylation MM constant (K 1R -1 ) Inverse of T 1 demethylation MM constant(k 1B -1 ) T 1 autophosphorylation rate K 1a Inverse of T 1 methylation MM constant (K 1R -1 )

Slices of 3D Surfaces of Parameter Space 5/10/2015Yang Yang, Candidacy Seminar

Validation 5/10/2015Yang Yang, Candidacy Seminar24 Time (s) Concentration (µM) Verify steady state NR solutions dynamically using DSODE for different stimulus ramps:

Violating and Restoring Perfect Adaptation 5/10/2015 Yang Yang, Candidacy Seminar 25 Step stimulus from 0 to 1e-3M at t=500s (5e+6,10) (1e+6,10) T 3 autophosphorylation rate (k 9 ) CheYp Concentration (µM) Inverse of T 3 MM constant (K 3R -1 ) Time (s)

Conditions for Perfect Adaptation: Kinetic Parameters 5/10/201526Yang Yang, Candidacy Seminar

Inverse of Methylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar27 T 0 autophosphorylation rate (k 0a ) Inverse of T 0 MM constant (K 0R -1 ) T 1 autophosphorylation rate (k 1a ) Inverse of T 1 MM constant (K1 R -1 )

Inverse of Methylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar28 T 2 autophosphorylation rate (k 2a ) T 3 autophosphorylation rate (k 3a ) Inverse of T 2 MM constant (K 2R -1 ) Inverse of T 3 MM constant (K 3R -1 )

Inverse of Methylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar29 LT 0 autophosphorylation rate (k 0al ) LT 1 autophosphorylation rate (k 1al ) Inverse of LT 0 MM constant (K 0LR -1 ) Inverse of LT 1 MM constant (K 1LR -1 )

Inverse of Methylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar30 LT 2 autophosphorylation rate (k 2al ) LT 3 autophosphorylation rate (k 3al ) Inverse of LT 2 MM constant (K 2LR -1 ) Inverse of LT 3 MM constant (K 3LR -1 )

Inverse of Demethylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar31 T 1 autophosphorylation rate (k 1a ) T 2 autophosphorylation rate (k 2a ) Inverse of T 1 MM constant (K 1B -1 ) Inverse of T 2 MM constant (K 2B -1 )

Inverse of Demethylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar32 T 3 autophosphorylation rate (k 3a ) T 4 autophosphorylation rate (k 4a ) Inverse of T 3 MM constant (K 3B -1 ) Inverse of T 4 MIM constant (K 4B -1 )

Inverse of Demethylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar33 LT 1 autophosphorylation rate (k 1al ) LT 2 autophosphorylation rate (k 2al ) Inverse of LT 1 MM constant (K 1LB -1 ) Inverse of LT 2 MM constant (K 2LB -1 )

Inverse of Demethylation MM Constant Autophosphorylation Rate 5/10/2015Yang Yang, Candidacy Seminar34 LT 3 autophosphorylation rate (k 12 ) LT 4 autophosphorylation rate (k 13 ) Inverse of LT 3 MM constant (K 2LB -1 ) Inverse of LT 4 MM constant (K 3LB -1 )

Methylation Catalytic Rate/ Demethylation Catalytic Rate = Constant 5/10/2015Yang Yang, Candidacy Seminar35 T 1 demethylation catalytic rate T 1 methylation catalytic rate T 2 demethylation catalytic rate T 2 methylation catalytic rate

Methylation Catalytic Rate/ Demethylation Catalytic Rate = Constant 5/10/2015Yang Yang, Candidacy Seminar36 T 3 demethylation catalytic rate T 2 methylation catalytic rate T 4 demethylation catalytic rate T 3 methylation catalytic rate

Methylation Catalytic Rate/ Demethylation Catalytic Rate = Constant 5/10/2015Yang Yang, Candidacy Seminar37 LT 1 demethylation catalytic rate LT 0 methylation catalytic rate LT 2 demethylation catalytic rate LT 1 methylation catalytic rate

Methylation Catalytic Rate/ Demethylation Catlytic Rate = Constant 5/10/2015Yang Yang, Candidacy Seminar38 LT 3 demethylation catalytic rate LT 2 demethylation catalytic rate LT 4 demethylation catalytic rate LT 3 demethylation catalytic rate

Summary 5/10/2015Yang Yang, Candidacy Seminar39 These conditions are consistent with those obtained in previous works from analysis of a detailed, two-state receptor model *.  The Inverse of Methylation MM constants linearly decrease with Autophosphorylation Rates  The Inverse of Demethylation MM constants linearly increase with Autophosphorylation Rates  The ratio of Methylation catalytic rates and demethylation catlytic rates for the next methylation level is constant for all methylation states * B. Mello et al. Biophysical Journal, (2003).

Conditions in Two-State Receptor Model 5/10/2015Yang Yang, Candidacy Seminar40  Receptor autophosphorylation rates are proportional to the receptor activity:  Only the inactive or active receptors can be methylated or demethylated. The association rates between receptors and CheR or CheB p are linearly related to the receptor activity, whiledissociation rates are independent with . Then the inverse of the methylation or demethylation MM constants are linearly related to the receptor activity:  The ratios between methylation catalytic rates and demethylation catalytic rates for the next methylation level are constant:  The phosphate transfer rates from CheA to CheB or CheY are proportional to receptor activities:

Conditions for Perfect Adaptation: Protein Concentrations

Summary of Protein Concentrations 5/10/2015Yang Yang, Candidacy Seminar42

Relationship Between Protein Concentrations 5/10/2015Yang Yang, Candidacy Seminar43 (M)

Relationship Between Protein Concentrations (cont’d) 5/10/2015Yang Yang, Candidacy Seminar44 (M)

Relationship between Protein Concentrations (cont’d) 5/10/2015Yang Yang, Candidacy Seminar45 (M)

Summary Preliminary observations : CheR concentration is restricted in a narrow small-value region while total receptor and CheY concentration can vary in a wide region. CheR concentration is proportional to the CheB concentration

Diversity of Chemotaxis Systems 5/10/2015Yang Yang, Candidacy Seminar47 Eg., Rhodobacter sphaeroides, Caulobacter crescentus and several rhizobacteria possess multiple CheYs while lacking of CheZ homologue. In different bacteria, additional protein components as well as multiple copies of certain chemotaxis proteins are present. Response regulator Phosphate “sink” CheY1 CheY2

Two CheY System 5/10/2015Yang Yang, Candidacy Seminar48 Exact adaptation in modified chemotaxis network with CheY 1, CheY 2 and no CheZ: CheY1 p (µM) Time(s) Requiring:  Faster phosphorylation/autodephosphorylation rates of CheY 2 than CheY 1  Faster phosphorylation rate of CheB

Conclusions 5/10/2015Yang Yang, Candidacy Seminar49 I.Successful implementation of a novel method for elucidating regions in parameter space allowing precise adaptation II.Numerical results for (near-) perfect adaptation manifolds in parameter space for the E. coli chemotaxis network, allowing determination of i.Conditions required for perfect adaptation, consistent with and extending previous works [1-3] ii.Numerical ranges for experimentally unknown or partially known kinetic parameters I.Extension to modified chemotaxis networks, for example with no CheZ homologue and multiple CheYs [1] Barkai & Leibler, Nature (1997). [2] Yi et al., PNAS (2000). [3] Tu & Mello, Biophys. J. (2003).

Future Work 5/10/2015Yang Yang, Candidacy Seminar50 Extension to other signaling networks  vertebrate phototransduction  mammalian circadian clock allowing determination of a) parameter dependences underlying robustness of adaptation b) plausible numerical values for unknown network parameters

Vertebrate Phototransduction 5/10/2015Yang Yang, Candidacy Seminar51 cGMP: cyclic GMP PDE: cGMP phosphodiesterase GCAP: guanylyl cyclase activating, Ca 2+ binding protein gc: guanylyl cyclase, which synthesis cGMP

Light Adaptation of Phototransduction 5/10/2015Yang Yang, Candidacy Seminar52 An intracellular recording from a single cone stimulated with different amounts of light. Each trace represents the response to a brief flash that was varied in intensity. At the highest light levels, the response amplitude saturates. (Neuroscience, Purves et al., 2001)

Kinetic Model for Vertebrate Phototransduction 5/10/2015Yang Yang, Candidacy Seminar53 Russell D. Hamer, Visual Neuroscience (2000)

Mammalian Circadian Clock 5/10/2015Yang Yang, Candidacy Seminar54  PERs transport CRYs to nucleus  CLOCK and BMAL1 bind together  CLOCK·BMAL1 binds to E box to increase Pers(Crys) transcription rates  E box is the sequence CACGTG of the PER1 and CRY1 genes  PERs bind with kinases CKIε/δ to be phosphorylated  Phosphorylated PERs bind with CRYs  Only phosphorylated PER·CRY· CKIε/δ can enter nucleus  Phosphorylated PER·CRY· CKIε/δ inhibit the ability of CLOCK·BMALI to enhance transcription  Increasing REV-ERB α levels repress BMAL1 transcription  Activator positively regulated BMAL1 transcription From Forger et al., PNAS (2003).

5/10/2015Yang Yang, Candidacy Seminar55

5/10/2015Yang Yang, Candidacy Seminar56

5/10/2015Yang Yang, Candidacy Seminar57

5/10/2015Yang Yang, Candidacy Seminar58

5/10/2015Yang Yang, Candidacy Seminar59

5/10/2015Yang Yang, Candidacy Seminar60

Checking Dynamics of CheY-P with Solutions 5/10/2015Yang Yang, Candidacy Seminar61 A B C D

Protein Concentration Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar62

Protein Concentration Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar63

Protein Concentration Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar64

Protein Concentration Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar65

Protein Concentration Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar66

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar67 T 2 autophosphorylation rate (k 2a ) T 3 autophosphorylation rate (k 3a ) inverse of T 2 MM constant (K 2R -1 ) inverse of T 3 MM constant (K 3R -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar68 T 2 autophosphorylation rate (k 2a ) T 3 autophosphorylation rate (k 3a ) inverse of T 2 MM constant (K 2R -1 ) inverse of T 3 MM constant (K 3R -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar69 T 1 autophosphorylation rate (k 1a ) T 2 autophosphorylation rate (k 2a ) inverse of T 1 M-M constant (K 1B -1 ) inverse of T 2 M-M constant (K 2B -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar70 T 3 autophosphorylation rate (k 3a ) T 4 autophosphorylation rate (k 4a ) inverse of T 3 M-M constant (K 3B -1 ) inverse of T 4 M-M constant (K 4B -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar71 LT 1 autophosphorylation rate (k 1al ) LT 2 autophosphorylation rate (k 2al ) inverse of LT 1 MM constant (K 1LB -1 ) inverse of LT 2 MM constant (K 2LB -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Reaction Rates Trend Shifting 5/10/2015Yang Yang, Candidacy Seminar72 LT 3 autophosphorylation rate (k 12 ) LT 4 autophosphorylation rate (k 13 ) inverse of LT 3 MM constant (K 2LB -1 ) inverse of LT 4 MM constant (K 3LB -1 ) Protein concentrations taken from SPO’s Protein concentrations taken from Mello-Tu’s

Slices of 3D Surfaces of Parameter Space 5/10/2015Yang Yang, Candidacy Seminar

Slices of 3D Surfaces of Parameter Space 5/10/2015Yang Yang, Candidacy Seminar

Slices of 3D Surfaces of Parameter Space Comparing Pair-Wise Relationship 5/10/2015Yang Yang, Candidacy Seminar75 T 1 autophosphorylation rate (k 1a ) Inverse of T 1 MM constant (K1 R -1 ) T 1 autophosphorylation rate (k 1a ) Inverse of T 1 MM constant (K 1B -1 )

E. coli and Bacteria Chemotaxis 5/10/2015Yang Yang, Candidacy Seminar76 Increasing attractants or Decreasing repellents