1

Supplementary Information

Supplementary Figures

Supplementary Figure S1. Cooperation and competition levels of the ecological groups at

different levels of competition and resource overlap. Bars represent standard error (bars are not shown for

sample sizes <2; for very small values of standard error bars are shown in red).

2

Supplementary Figure S2: The frequency of resource overlap values between ecologically associated

(black) and non-associated (white) species pairs. As shown, ecologically associated pairs differ in the

pattern of distribution of their resource overlap values. This further supports a non-random distribution of

metabolic-demand similarity between ecologically-associated and non-associated pairs whereas the pick

observed for moderate values (~0.5) arises mainly from the contribution of co-occurring pairs.

3

Supplementary Figure S3. Growth curves of individual and pairwise combinations across different media.

Growth combinations are ordered as in Supplementary Tables S5 and S6. The title of each graph indicates

the species combination and the medium (that is LA,PRM indicates Listeria innocua- Agrobacterium

tumefaciens in primary medium). Abbreviations are as in Supplementary Table S6. Gold and orange lines

represent the individual growth of the first and second pair members, respectively (that is, in LA

combination L. innocua is shown in gold and A. tumefaciens is shown in orange). Blue line represents co-

growth. Red line represents the sum of individual growth at the manually selected exponential growth

phase (OD values of first species at time0 were subtracted). Only successful predictions are shown (√ at

Supplementary Table S5).

4

Supplementary Tables

Supplementary Table S1: Frequency of directional give-take relationships across bacterial families (top

10 combinations). The table describes the frequency of inter-family give-take interactions, considering the

total number of pairwise inter-class combinations. In order to have groups of similar size, some groups

(e.g., Actinobacteria) describe the phylum level classification.

Giving family Receiving family Total number of inter-

family interactions

Total number of

directional give-take

inter-family

interactions

Fraction of directional

give-take inter-family

interactions

Lactobacillales Alpha/others

proteobacteria 24 13 0.54

Clostridia Bacillales 36 20 0.56

Clostridia Betaproteobacteria 40 24 0.6

Clostridia

Hyperthermophilic

bacteria 8 5 0.63

Spirochete

Alpha/others

proteobacteria 8 5 0.63

Clostridia Deltaproteobacteria 12 8 0.67

Clostridia Bacteroides 12 9 0.75

Clostridia Actinobacteria 32 26 0.81

Clostridia

Alpha/others

proteobacteria 16 13 0.81

Clostridia Epsilonproteobacteria 12 11 0.92

5

Supplementary Table S2. Description of model species and selected properties. The table is sorted

according to the fraction of winning events. Species seed ids are as in14

. Fractions of regulatory genes were

retrieved from39

. General environmental complexity estimates (1- obligatory symbionts; 2- specialized; 3-

aquatic; 4- facultative host-associated; 5- multiple; 6- terrestrial species) were obtained from40

. Minimal

doubling time information was retrieved from41

.

Species' name Species' seed id

Maximal

Biomass

Production

Rate

(MBR)

Fraction of

winning

events

Fraction of

regulatory

genes

Estimate of

environmental

diversity

Minimal

doubling

time

Dehalococcoides

ethenogenes 195 Core243164_3 4.3 0 19

Thiomicrospira

crunogena XCL-2 Core39765_1 4 0 1

Bartonella

bacilliformis KC583 Core360095_3 3.8 0

Mycoplasma

genitalium G-37 Core243273_1 1.2 0 0 1 12

Anaplasma

marginale str. St.

Maries Core234826_3 4.7 0 21.6

Buchnera aphidicola

str. APS

(Acyrthosiphon

pisum) Core107806_1 3.5 0 0 1

Treponema pallidum

subsp. pallidum str.

Nichols Core243276_1 0.9 0

Borrelia burgdorferi

B31 Core224326_1 3.6 0 4 4

Bifidobacterium

longum NCC2705 Core206672_1 11.4 0.1 0.1 4 1.51

Wolbachia sp.

endosymbiont of

Drosophila

melanogaster Core163164_1 13 0.1

Coxiella burnetii

RSA 493 Core227377_1 8.8 0.1 5 8

Ehrlichia

ruminantium str.

Gardel Core302409_3 14 0.1

Rickettsia

prowazekii str.

Madrid E Core272947_1 10.3 0.1

Gluconobacter

oxydans 621H Core290633_1 8.1 0.1 0.94

Blochmannia

floridanus Core203907_1 11.7 0.1 1 36

Thiomicrospira

denitrificans ATCC

33889 Core326298_3 7.6 0.1

Tropheryma Core203267_1 21.5 0.1

6

whipplei str. Twist

Aquifex aeolicus

VF5 Core224324_1 8 0.1 0 2 1.8

Helicobacter pylori

26695 Core85962_1 23.3 0.2 0 4 2.4

Streptococcus

pneumoniae R6 Core171101_1 31.3 0.2 0 4

Streptococcus

pneumoniae TIGR4 Core170187_1 31.3 0.2 0 4 0.5

Thermoanaerobacter

sp. X514 Core399726_4 34.5 0.2

Carboxydothermus

hydrogenoformans

Z-2901 Core246194_3 24 0.2 2

Onion yellows

phytoplasma OY-M Core262768_1 20.3 0.2

Thermotoga

maritima MSB8 Core243274_1 21 0.2 0 2 1.2

Mycoplasma

pulmonis UAB CTIP Core272635_1 22 0.2 0 1 1.5

Streptococcus

thermophilus

CNRZ1066 Core299768_3 29.1 0.2

Ureaplasma parvum

serovar 3 ATCC

700970 Core273119_1 16.1 0.2

Legionella

pneumophila subsp.

pneumophila str.

Philadelphia 1 Core272624_3 24.9 0.2 0 3.3

Idiomarina

loihiensis L2TR Core283942_3 26.9 0.2

Xylella fastidiosa

9a5c Core160492_1 16.5 0.3 0 1 5.13

Campylobacter

jejuni subsp. jejuni

84-25 Core360110_3 26.6 0.3

Neisseria

gonorrhoeae FA

1090 Core242231_4 25 0.3 0.58

Campylobacter

jejuni subsp. jejuni

NCTC 11168 Core192222_1 26.6 0.3 5 1.5

Chlamydia

trachomatis D/UW-

3/CX Core272561_1 23.2 0.3 1 24

Haemophilus

influenzae Rd KW20 Core71421_1 41.2 0.3

Leifsonia xyli subsp.

xyli str. CTCB07 Core281090_3 46 0.3 5

Symbiobacterium

thermophilum IAM

14863 Core292459_1 33.8 0.3 4.2

Desulfovibrio

desulfuricans G20 Core207559_3 24.6 0.3 0

7

Elusimicrobium

minutum Pei191 Core445932_3 30.6 0.3

Chlamydophila

pneumoniae AR39 Core115711_7 22.9 0.3 0 1

Campylobacter

jejuni subsp. jejuni

CF93-6 Core360111_3 26.6 0.3

Bdellovibrio

bacteriovorus

HD100 Core264462_1 21.9 0.3 1.4

Zymomonas mobilis

subsp. mobilis ZM4 Core264203_3 21.8 0.3 2

Pseudomonas putida

KT2440 Core160488_1 78.8 0.4 0.1 5 1.1

Bordetella pertussis

Tohama I Core257313_1 46.9 0.4 0.1 1 3.8

Lactococcus lactis

subsp. lactis Il1403 Core272623_1 63.8 0.4 0.1 5 0.7

Lactobacillus

plantarum WCFS1 Core220668_1 80.7 0.4 0.1 4 1.6

Kineococcus

radiotolerans

SRS30216 Core266940_1 48.7 0.4

Bacteroides fragilis

YCH46 Core295405_3 28 0.4 0.63

Clostridium tetani

E88 Core212717_1 56.4 0.4 0 5 0.5

Cytophaga

hutchinsonii ATCC

33406 Core269798_12 48 0.5 0

Salinibacter ruber

DSM 13855 Core309807_5 20.7 0.5 14

Clostridium

acetobutylicum

ATCC 824 Core272562_1 83.8 0.5 0.1 5 0.58

Mannheimia

succiniciproducens

MBEL55E Core221988_1 87.4 0.5 0.6

Anaeromyxobacter

dehalogenans 2CP-

C Core290397_13 72.9 0.5 9.2

Francisella

tularensis subsp.

tularensis Schu 4 Core177416_3 57.3 0.5 3

Caulobacter

crescentus CB15 Core190650_1 36.3 0.5 0.1 3 1.5

Nitrosococcus

oceani ATCC 19707 Core323261_3 44.7 0.5

Listeria

monocytogenes

J0161 Core393130_3 55.5 0.5

Nitrosomonas

europaea ATCC

19718 Core228410_1 41.3 0.5 0 5 18.5

Francisella Core401614_5 59.8 0.5 3

8

tularensis subsp.

novicida U112

Frankia sp. Ccl3 Core106370_11 71.8 0.5

Neisseria

meningitidis MC58 Core122586_1 30 0.5 0 4

Acinetobacter sp.

ADP1 Core62977_3 92 0.5

Listeria

monocytogenes FSL

J1-194 Core393117_3 55.5 0.5

Nocardia farcinica

IFM 10152 Core247156_1 53.2 0.6 3

Magnetospirillum

magneticum AMB-1 Core342108_5 46.3 0.6 0

Staphylococcus

aureus subsp. aureus

N315 Core158879_1 94.8 0.7 0 4 0.4

Methylococcus

capsulatus str. Bath Core243233_4 55.5 0.7 1.87

Acinetobacter

baumannii ATCC

17978 Core400667_4 73.8 0.7

Streptomyces

coelicolor A3(2) Core100226_1 94.3 0.7 0.1 5 2.2

Corynebacterium

glutamicum ATCC

13032 Core196627_4 64.5 0.7 5 1.2

Flavobacterium

johnsonia

johnsoniae UW101 Core376686_6 41.4 0.7

Thiobacillus

denitrificans ATCC

25259 Core292415_3 61.5 0.7

Leptospira

interrogans serovar

Copenhageni str.

Fiocruz L1-130 Core267671_1 64.1 0.7

Listeria innocua

Clip11262 Core272626_1 94 0.7 0.1 5 0.6

Pseudomonas

fluorescens PfO-1 Core205922_3 104.9 0.7

Pseudoalteromonas

haloplanktis

TAC125 Core326442_4 48.8 0.7 0.5

Rhizobium

leguminosarum bv.

viciae 3841 Core216596_1 105.3 0.8

Staphylococcus

aureus subsp. aureus

COL Core93062_4 123.9 0.8

Rhodopseudomonas

palustris CGA009 Core258594_1 69.3 0.8 9

Methylobacillus

flagellatus KT Core265072_7 76 0.8 2

Agrobacterium Core176299_3 91.7 0.8 0.1 5

9

tumefaciens str. C58

Rubrobacter

xylanophilus DSM

9941 Core266117_6 70.7 0.8 3.85

Brucella melitensis

16M Core224914_1 72.8 0.8 0 4 2

Vibrio cholerae

O395 Core345073_6 127.3 0.8 0.2

Burkholderia

pseudomallei

K96243 Core272560_3 99.5 0.8 1

Ralstonia

solanacearum

GMI1000 Core267608_1 135.8 0.8 0.1 5 4

Vibrio vulnificus

YJ016 Seed196600_1 128.1 0.8 0

Staphylococcus

aureus subsp.

aureus NCTC 8325 Core93061_3 94.8 0.8

Yersinia pestis

Pestoides F Core386656_4 104.2 0.8

Clostridium

beijerincki

beijerinckii NCIMB

8052 Core290402_34 120.3 0.8

Pseudomonas putida

GB-1 Core76869_3 104.7 0.8

Vibrio

parahaemolyticus

RIMD 2210633 Core223926_1 118.7 0.8 0 4 0.2

Yersinia pestis

CO92 Core214092_1 103.5 0.8 0.1 5 1.25

Mycobacterium

tuberculosis H37Rv Core83332_1 69.4 0.8 0 4 19

Polaromonas sp.

JS666 Core296591_1 70.4 0.8

Staphylococcus

aureus subsp.

aureus Mu50 Core158878_1 94.8 0.8 0 4

Bradyrhizobium

japonicum USDA

110 Core224911_1 107.2 0.9 0.1 4 20

Shewanella

frigidimarina

NCIMB 400 Seed318167_10 92.5 0.9

Bacillus subtilis

subsp. subtilis str.

168 Opt224308_1 211.7 0.9 0.1 6 0.43

Escherichia coli

W3110 Core316407_3 247.1 0.9 4

Pseudomonas

aeruginosa PAO1 Core208964_1 158.4 0.9 0.1 5

Sinorhizobium

meliloti 1021 Core266834_1 202.8 0.9 0.1 5 1.5

Burkholderia Core269482_1 150.6 0.9

10

cepacia R1808

Klebsiella

pneumoniae MGH

78578 Core272620_3 205.4 0.9

Bacillus anthracis

str. 'Ames Ancestor' Core261594_1 167.7 0.9

Listeria

monocytogenes

EGD-e Core169963_1 87.9 0.9 0.1 5 1

Shigella flexneri 2a

str. 2457T Core198215_1 183.2 0.9 4

Shigella dysenteriae

M131649 Core216598_1 145.6 0.9

Bacillus anthracis

str. Ames Core198094_1 171.2 0.9 0.1 0.5

Photobacterium

profundum SS9 Core298386_1 156.1 1 2.5

Photorhabdus

luminescens subsp.

laumondii TTO1 Core243265_1 116.5 1 0.1 0.5

Vibrio cholerae O1

biovar eltor str.

N16961 Core243277_1 134.5 1 0 4 0.2

Salmonella

typhimurium LT2 Core99287_1 229 1 0.1 4 0.4

Escherichia coli K12 Core83333_1 250.2 1 0.1 4 0.35

Shewanella

oneidensis MR-1 Seed211586_1 91.3 1 0 5 0.66

11

Supplementary Table S3. The list of EnvO niches used in the analysis and the number of assigned

samples.

Envo ID Niche description Number of samples ENVO:00000063 water body 541 ENVO:00001998 soil 489 ENVO:00002007 sediment 276 ENVO:00002006 water 258 ENVO:00002044 sludge 113

ENVO:01000009 biotic mesoscopic physical

object 98 ENVO:00000023 stream 93 ENVO:00002002 food 66 ENVO:00002264 waste 58 ENVO:00000076 mine 52 ENVO:00002031 anthropogenic habitat 52 ENVO:00000176 elevation 48 ENVO:00002009 terrestrial habitat 48 ENVO:00001995 rock 40 ENVO:01000001 mud 37 ENVO:00002985 oil 36 ENVO:00000043 wetland 34 ENVO:00000131 glacial feature 25 ENVO:02000019 bodily fluid 23 ENVO:00000104 undersea feature 21 ENVO:00000013 cave system 20 ENVO:00000479 mouth 18 ENVO:00002204 contamination feature 18 ENVO:00002170 compost 17 ENVO:00000094 volcanic feature 14 ENVO:00000303 coast 13 ENVO:00000073 building 12 ENVO:00000291 drainage basin 12 ENVO:00000026 well 12 ENVO:00002005 air 10 ENVO:00000077 agricultural feature 10 ENVO:00003030 silage 9 ENVO:00002008 dust 8 ENVO:00003869 straw 8 ENVO:00000463 harbor 7 ENVO:00000182 plateau 6 ENVO:00000309 depression 6 ENVO:00000395 channel 5 ENVO:00000130 reef 5 ENVO:00002982 clay 5 ENVO:00010505 aerosol 3 ENVO:00000091 beach 3

12

ENVO:01000010 abiotic mesoscopic physical

object 3 ENVO:00000097 desert 3 ENVO:00002272 waste treatment plant 3 ENVO:00000475 inlet 3 ENVO:00002226 borehole 3 ENVO:00002040 wood 2 ENVO:00000086 plain 2 ENVO:00000304 shore 2 ENVO:00000175 karst 2 ENVO:00002000 slope 2

ENVO:00000049 volcanic hydrographic

feature 2 ENVO:00000062 populated place 1 ENVO:00002039 bone 1 ENVO:00000474 cut 1 ENVO:00000562 park 1 ENVO:00005738 foam 1 ENVO:00000098 island 1

13

Supplementary Table S4. IMM defined medium and its in silico representation. Modifications of IMM

were done using the same algorithm used for selecting a minimal media (Supplementary Methods), aiming

to find the minimal set of metabolites which are necessary to support co-growth. The same in silico media

were used for all pairwise combinations. Metabolite In vitro medium In silico medium

Thiamin + +

D-Methionine + +

Magnesium + +

L-Valine + +

L-Isoleucine + +

L-Leucine + +

L-Histidine + +

Calcium + +

D-Glucose-6-phosphate + +

Potassium + +

Citrate + +

L-Arginine + +

L-Tryptophan + +

L-Phenylalanine + +

Biotin + +

Riboflavin + +

Adenine + +

Pyridoxal + +

Nicotinamide_D-ribonucleotide + +

L-Glutamine + +

L-Cysteine + +

Lipoic acid + -

para-aminobenzoic acid + -

Oxygen + +

Cytosine - +

Zinc - +

Cobalt - +

Fe2+ - +

Chloride - +

Sulfate - +

Copper2 - +

Manganese - +

Spermidine - +

gly-asn-L - +

sn-Glycerol-3-phosphate - +

octadecanoate - +

14

Supplementary Table S5. Predicted and observed co-growth shifts. For predicted and observed co-growth

combinations we compared the ratio between the Sum of the Individual Growths (SIG) and the co-growth

(CG) across the three media (primary, negative, and positive). The SIG/CG ratio in the negative and

positive media is compared to the ratio in the primary media where negative and positive shifts refer to an

increase or decrease in this ratio, respectively. Colored columns represent a predicted directional shift in the

corresponding interaction-designed media. Red indicates a predicted negative shift in the negative media

and green indicates a predicted positive shift in the positive media. Observations: Table entries marked

with '√' and 'X' represent corresponding or non-corresponding shifts as observed in laboratory co-growth

experiments. Colored '√' symbols represent TP predictions; non-colored '√' symbols represent TN

predictions; Colored 'X' symbols represent FP predictions; non-colored 'X' represent FP predictions; For

observations, SIG/CG ratio was calculated according to OD values recorded in logarithmic growth and the

corresponding growth rates, as described in Supplementary Table S6. Predicted and observed SIG/CG

values are shown in Supplementary Table S6. Growth curves are presented at Supplementary Figure S3.

Positive shift Negative shift Species-pair

√ √ Agrobacterium tumefaciens-

Listeria innocua

√ √ Agrobacterium tumefaciens-

Escherichia coli

√ √ Agrobacterium tumefaciens-

Pseudomonas aeruginosa

X √ Agrobacterium tumefaciens-

Bacillus subtilis

√ X Listeria innocua-Escherichia

coli

X X Listeria innocua-Pseudomonas

aeruginosa

X X Listeria innocua-Bacillus

subtilis

√ √ Escherichia coli-Pseudomonas

aeruginosa

X √ Escherichia coli-Bacillus

subtilis

√ √ Pseudomonas aeruginosa-

Bacillus subtilis

6/10 7/10 True predictions

15

Supplementary Table S6. Calculated values for predicted and observed co-growth shifts. Values show the

SIG/CG ratio (SIG: Sum of the Individual Growth; CG: Co Growth). PRM, NM, PM: Primary, Negative

and Positive Medium, respectively. L: Listeria innocua; A: Agrobacterium tumefaciens ; E: Escherichia

coli; P: Pseudomonas aeruginosa; B: Bacillus subtilis.

Computational

predictions Experimental observations

Growth rate ratio‡ Growth ratio†

PRM NM PM PRM NM PM PRM NM PM

A-L 1.06 1.12 0 0.73 2.08 0.5 0.91 2.09 0.64

A-E 1.25 1.38 1.04 1.71 2.39 0.89 1.54 2.69 1.49

A-P 1.1 1.12 0.98 1.26 1.5 1.15 1.65 2.47 1.41

A-B 1.34 1.43 0.77 1.75 2.42 4.15 2.66 4.27 2.85

L-E 1.21 1.21 0.9 1.56 1.41 0.53 1.5 1.64 0.9

L-P 0.91 0.91 0.7 1.31 1.37 0.62 1.0 1.05 1.0

L-B 1.22 1.20 0.78 1.1 1.18 1.11 1.03 1.34 1.36

E-P 1.43 1.36 1.4 1.1 1.03 0.65 1.38 0.87 1.27

E-B 1.49 1.56 1.28 1.2 1.59 1.63 1.54 1.78 2.05

P-B 1.17 1.18 1.06 2.43 2.47 1.33 2.18 2.36 1.61

‡ Growth rate ratio was calculated by comparing ∆OD/∆time ratio in SIG and CG during exponential

growth. Exponential growth was determined for each experiment independently as shown in

Supplementary Figure S3. For SIG, ∆OD was calculated as sum ∆OD of both species.

† Growth ratio was calculated by comparing the OD in SIG and CG at a constant time point (half time of

the experiment). For SIG, OD was calculated as sum OD of both species at the selected time point. OD

values at time0 (the beginning of the experiments) were subtracted.

Supplementary Table S7. Interactions between Salinibacter ruber and Haloquadratum walsbyi across

different media.

Presence

of DHA in

the media

Co-

growth

Individual

growth of

H. walsbyi

Individual

growth of

S. ruber

PCMS Cooperative

interaction

COMPM + 69.1 60.2 20.1 0.57 -

Reduced

media

(+ DHA)

+ 27.3 11.7 8.3 -0.88 +

Reduced

media

-DHA

- 27.3 0 8.3 NA +

16

Supplementary Table S8. Characterization of species-specific metabolic computationally-designed

environments ({VCOMP,A}, Supplementary Methods). The full list of species-specific environments is

provided at Supplementary Data 8.

* Highest absolute value

10 most frequent

metabolites across

the 118 species-

specific environments

10 most rare metabolites

across the 118 species-

specific environments

10 metabolites with the

highest* mean flux at

optimal conditions (typical

limiting factors)

Copper2

beta-

Methylglucoside_C7H14O6 Oxygen

Sulfate

D-

Glucosamine_C6H14NO5 H+

Fe3 Decanoic_acid_C10H19O2 D-Glucose

Magnesium

D-O-

Phosphoserine_C3H7NO6P L-Glutamate

Zinc

(R,R)-

Tartaric_acid_C4H4O6 sn-Glycerol_3-phosphate

Manganese Propanoate_C3H5O2 NH3

Cobalt Isocitrate_C6H5O7 Fumarate

Potassium

(R,R)-Butane-2,3-

diol_C4H10O2 D-Fructose

Calcium Nicotinamide_C6H6N2O Nitrate

Fe2

beta-

Methylglucoside_C7H14O6 L-Serine

17

Supplementary Table S9. Characterization of pair-specific metabolic environments (rich ({VCOMP, AB})

and poor ({VMM, AB}), Supplementary Methods). The full lists of pair-specific rich and poor environments

are provided at Supplementary Data 9 and 10, respectively.

* Found across all environments

** calculated by subtracting the frequency of metabolite at poor media from its frequency in rich media

10 most frequent

metabolites across both

minimal and rich pairwise

environments*

10 frequent metabolites in

rich media that are absent

in poor, cooperation

inducing, media**

Magnesium_Mg ala-L-glu-L_C8H13N2O5

Sulfate gly-glu-L_C7H11N2O5

Chloride_Cl Ala-Gln_C8H15N3O4

Potassium_K H+_H

Calcium_Ca ala-L-asp-L_C7H11N2O5

Fe2+_Fe Ala-Leu_C9H18N2O3

Manganese_Mn Sodium_Na

Cobalt_Co Gly-Leu_C8H16N2O3

Copper2_Cu gly-pro-L_C7H12N2O3

Zinc_Zn

L-

alanylglycine_C5H10N2O3

18

Supplementary Table S10. Frequency of symmetrical interaction events under minimal growth media

with different thresholds for biomass production of the system.

Fraction of cooperative events within different

ecological groups

%BPR

(out of BPR

in COMPM)

Total number of

cooperative

events

(fraction of

unidirectional

events¤)

§N=3160

Non-

associated

N=2512

Niche-

associated‡

N=536

Co-

occurring

N=84

Mutually-

exclusive

N=28

Rich

Media

(COMP

M)

100% 0 0 0 0 0

75% 1814(0.65) 0.52 0.77 0.85 0.93

50% 1466(0.88) 0.4 0.69 0.71 0.86

25% 1279(0.93) 0.36 0.58 0.6 0.79

10% 1293(0.94) 0.37 0.57 0.51 0.71

Reduced

media

Intersection

† 656(0.96) 0.16 0.38 0.35 0.36

¤ Unidirectional events refer to cooperative interactions where only one of the pair members is a giver and

the other is a taker.

§N represents all possible combinations in a specific group

‡ Niche associated pairs do not include co-occurring and mutually exclusive pairs

†Intersection medium for a pair of species is calculated as the intersection of uptake reactions from their

individual COMPMs

19

Supplementary Table S11. Frequency of symmetrical interaction events under minimal growth media

with different thresholds for biomass production of the system and the compartments in the system.

Fraction of cooperative events within different

ecological groups %BPR

(out of BPR

in COMPM)

Total number

of cooperative

events

(fraction of

unidirectional

events)

N=3160

Non-

associated

N=2512

Niche-

associated

N=536

Co-

occurring

N=84

Mutually-

exclusive

N=28

10%‡ (10%†) 1630(0.37) 0.52 0.51 0.44 0.71

25%‡ (25%†) 1768(0.40) 0.54 0.61 0.61 0.79

50%‡ (50%†) 2325(0.51) 0.72 0.8 0.82 0.89

75%‡ (75%†) 1156(0.52) 0.4 0.25 0.26 0.18

‡ the threshold for the feasible solution of the multi-species system

† the threshold for the feasible solution of the multi-species system in each compartment in the multi-

species system

20

Supplementary Table S12. Computational predictions for the effect of reducing and removing

computationally-predicted limiting factors from IMM media. L, A – predictions for the individual growth

of Listeria innocua and Agrobacterium tumefaciens, respectively across the media tested; LA – co-growth

prediction. Values in red indicate a change >±0.05 in growth and growth ratio in comparison to values

predicted for the original IMM.

Reduced metabolite (Vi,

Min_FVA =-10*)

Full removal of the metabolite

(Vi, Min_FVA = 0)

L A LA Growth

ratio

(L+A)/LA

L A LA Growth

ratio

(L+A)/LA

Growth at the

original IMM**

36

52

83

1.06

36 52 83 1.06

Isoleucine 36 48

79

1.06 36 47 78 1.05

Histidine† 36 49 77 1.1 36 0

36

1

Glutamine 32

48 76 1.05 30 46 73 1.04

Cysteine† 31 52 77 1.08 29

52

75

1.08

Glucose 21 52

65

1.12

0

52

61

0.85

*Similar behavior is observed for additional Vi, Min_FVA (-50 < Vi, Min_FVA < 0).

** For all metabolites in the table Vi, Min_FVA =-50.

† Full reduction of histidine and cysteine has the most drastic effect on the growth predictions of

Agrobacterium tumefaciens and Listeria innocua, respectively.

21

Supplementary Table S13. Predicted and observed growth and co-growth shifts. Each cell's color

represents the computationally-predicted growth shift in the designed media: red indicates reduced growth

(growth predictions section) and reduced growth ratio (growth ratio section); grey represents no growth

reduction (growth predictions section) and no growth ratio change (growth ratio section; black color in the

corresponding cells at Supplementary Table S12); dark green represents elevated ratio (red color in the

corresponding cells at Supplementary Table S12). Observations: Table entries marked with '√' and 'X'

represent corresponding or non-corresponding shifts as observed in laboratory co-growth experiments.

Growth shift is defined as a change of >±0.25 in growth and growth ratio in comparison to values detected

at the original IMM. The corresponding experimental results are provided at Supplementary Table S14. L -

Listeria innocua; A - Agrobacterium tumefaciens.

Growth predictions

L A

Growth ratio

(L+A)/LA

Histidine

(Vi, Min_FVA = 0)

√ √ √

Cysteine

(Vi, Min_FVA = 0) √ √ √

Glucose

(Vi, Min_FVA =-1 0) X √ √

Glucose

(Vi, Min_FVA = 0)

√ √ X

Supplementary Table S14. Observed growth and co-growth shifts. Values indicate the maximal OD in the

experiments. Experiments were conducted as described in the Supplementary Methods section and in

Supplementary Note 1.

L A LA (L+A)/LA

Growth at the

original IMM**

0.2 0.19 0.5 0.8

Histidine

(Vi, Min_FVA = 0)

0.43 0.13 0.59 0.95

Cysteine

(Vi, Min_FVA = 0)

0.02 0.22 0.28 0.86

Glucose

(Vi, Min_FVA =-1 0)

0.21 0.21 0.3 1.4

Glucose

(Vi, Min_FVA = 0)

0.01 0.19 0.15 1.25

** For all metabolites at the table Vi, Min_FVA =-50.

22

Supplementary Notes

Supplementary Note 1: Experimental and computational co-growth analyses for 10

bacterial pairs in interaction-specific media.

Individual and co-growth experiments were conducted for five bacterial species,

all non-pathogenic and capable of growing in IMM and their 10 corresponding pairwise

combinations. Growth experiments for each individual and pairwise combination were

conducted in three media: IMM – a chemically defined minimal medium41

(termed

primary medium), a "negative" medium designed to induce a negative shift towards

increased competition (by adding thymidine and xylose) and a "positive" medium

designed to induce a positive shift towards less competition (by subtracting thiamine and

glucose). The "negative" and "positive" media were designed as described in the

Supplementary Methods section, representing the most generic media for the induction of

a shift in the pattern of co-growth across most growth combinations (Supplementary Data

11). The observed shift in the co-growth pattern (in comparison to co-growth in the

primary medium) was successfully predicted in 65% of the experiments, with precision

0.75 and recall 0.8 (Supplementary Table S5) The species and their seed models are the

following: Listeria innocua Clip11262 (Core272626_1), Agrobacterium tumefaciens str.

C58 (Core176299_3), Escherichia coli K12 (Core83333_1), Pseudomonas aeruginosa

PAO1 (Core208964_1), Bacillus subtilis str. 168 (Opt224308_1).

23

Supplementary Note 2: using systematic data sources for estimating the ecological

relevance of win-lose predictions

For each pair of species we looked at the outcome of the competition and defined

species as winners or losers according to their growth rate (the faster species is the

winner as in Figure 2a in the main text). For each species we calculated its fraction of

winning events across all its co-growth experiments. The list of species' competition

values is provided at Supplementary Table S2. Top "winners" include ecologically

diverse fast growers such as Escherichia coli, Salmonella typhimurium, Vibrio cholerae

and Pseudomonas aeruginosa. Species with a low mean competition score include slow

grower pathogens such as Mycoplasma genitalium and Borrelia burgdorferi and

obligatory symbionts as Buchnera aphidicola.

Maximal growth rate information is available for 66 species in the data, retrieved

according to manual survey of the scientific literature41

. The matrix at Figure 2b contains

all these species for which doubling time information is available, sorted according to

their doubling time. To study the statistical significance of the win-lose division in the

experimentally-driven matrix we compared the strength of green-red division (win-lose)

in the original matrix to the red-green division in 1000 random matrices. To produce such

random matrices, the order of species was randomly permuted 1000 times and for each

corresponding matrix we counted how many winners were mapped to the upper triangle

("green" side). In the original matrix we observe 1256 true classifications (the

experimentally faster is the winner), which is higher than the number of true

classifications observed in 998 random matrices (T-test, P value 0.002).

24

To systematically study the biological relevance of the competition score we

looked at the correlation between competition values and environmental diversity,

considering two independent measures – fractions of regulatory genes were taken from39,

describing the fraction of transcription factors out of the total number of genes in the

genome - an indicator of environmental variability40

. General environmental complexity

estimates were also obtained from50

where the natural environments of bacterial species

were categorized based on the NCBI classification for bacterial lifestyle50

and ranked

according to the complexity of each category (1- obligatory symbionts; 2- specialized; 3-

aquatic; 4- facultative host-associated; 5- multiple; 6- terrestrial species). We observe a

significant correlation between environmental diversity and winning potential,

considering both absolute and partial mean competition score. Correlation values in a

spearman correlation test between competition values against the two measures of

environmental diversity:

Mean competition score versus the fraction of regulatory genes: 0.6 (P value 9e-

6)

Mean competition score versus NCBI estimate for environmental diversity: 0.46

(P value 2e-3)

Mean competition score versus experimentally recorded minimal doubling time: -

0.34 (P value 5e-3)

Experimental growth rates and lifestyle annotations are provided at Supplementary Table

S2.

25

Supplementary Note 3: Simulating co-growth of Salinibacter ruber and Haloquadratum

walsbyi.

We studied the effect of the media on the type of interaction between Salinibacter

ruber and Haloquadratum walsbyi – two halophylic species that co-exist in salterns. We

chose to focus on these species as a synergistic interaction between them was

documented, where it was suggested that the improved growth of H. walsbyi can be

explained by the uptake of dihydroxyacetone (DHA) produced by S. ruber24

. We

computationally studied the interaction between the species in a poor medium, with and

without DHA. Starting from competition-inducing medium (COMPM), reduction was

done using the algorithm used for computing cooperation-inducing media (Methods,

main text and Supplementary Methods) where we looked for the set of metabolites that

allows a feasible solution for each of the species individually (achieving at least 10% of

the corresponding growth in COMPM). In our simulations, co-growth of both species in a

rich medium (COMPM, Methods and Supplementary Methods) revealed no cooperative

interaction (PCMS > 0, Supplementary Table S7). Looking at the content of the

metabolites in the media we observed that DHA, externally provided to the system, is

consumed by the multi-species system and hence the contribution of its transfer between

the species to their growth potential is concealed. The dependence between H. walsbyi

and S. ruber for DHA supply is revealed when reducing the medium. As suggested in the

experimental studies, we observed that the growth of H. walsbyi becomes possible only

26

when adding DHA into the medium or by adding S. ruber to the community

(Supplementary Table S7).

Supplementary Note 4: Relating the designed media to true ecological conditions

Throughout most of this analysis, simulations are conducted in computationally-

derived, designed media. In order to examine the ecological relevance of the designed

media we first tested whether ecologically related species exhibit similarity in their media

({VCOMPM,A}, Supplementary Methods), as can be expected from the demonstrated

similarity in the metabolic pathways of co-occurring species30. Indeed, we observe that

the resource overlap (Methods) between ecologically associated species is significantly

higher than the resource overlap between none ecologically related species (P value 1.5

e-8 in a one-sided Wilcoxon test; median values for resource overlap are 0.41 and 0.46,

respectively). We then characterized the rate of occurrence of different metabolites across

species-specific computationally-designed environments, as well as identified typical

growth-limiting factors. Notably, the 10 most frequent compounds (listed at

Supplementary Table S8), include essential inorganic compounds as metals and salts. In

contrast, species show high diversity in their carbon sources (Supplementary Table S8).

In order to identify species-specific limiting factors we looked at the typical flux

of each metabolite (that is, the mean Vi, Min_FVA across all species, Supplementary

Methods), where a low typical flux indicates that a compound is a limiting factor.

Typical limiting factors include oxygen, glucose and nitrogen sources, in correspondence

with experimental knowledge. Alternatively, the widely-distributed inorganic compounds

in Supplementary Table S8, are all consumed at a typically low levels (all show mean Vi,

27

Min_FVA > -1 in comparison to mean Vi, Min_FVA < -20 for the highly consumed metabolites

in the left column, Supplementary Table S9).

Finally, we studied the distribution of metabolites in the pair-specific, rich

({VCOMPM, AB}) and poor ({VCOOPM, AB}) environments (Methods and Supplementary

Methods). Metabolites which are frequent at both pair-wise media are inorganic essential

compounds (Supplementary Table S9). Notably, metabolites which are typical of the rich

media but are missing from the poor, cooperation inducing media are typically derivates

of amino acids, representing a set of metabolic products that can be produced by one of

the species and then transferred to its pair members27

.

Supplementary Note 5: The use of various thresholds for determining a feasible growth

solution in minimal, cooperation-inducing, media

A Cooperation-inducing Media (COOPM) is defined here as a set of metabolites

that allows a feasible solution with positive growth rate, such that the removal of any

metabolite from the set would make such solution infeasible. We examined several

growth requirements threshold ranging between 10% of the BPR found in competition-

inducing (COMPM), rich media (the minimal media reported in the main text) to 100%

(as in the original COMPM environment, main text). All reduced media reveal

cooperative solutions (Supplementary Table S10). At all solutions, ecologically

associated pairs of species, and in particular mutually exclusive pairs exhibit higher level

of cooperation in comparison to non-associated pairs. The same trends were observed

when a minimal, cooperation-inducing, medium was calculated as the intersection of the

exchange reactions in COMPM.

28

In a multi-species system, we defined a symmetrical interaction as such where

both A and B are "givers" (and "takers"), that is both species improve their growth in

comparison to individual growth. Notably, this definition is permissive as A and B can

show variability in the extent to which their growth is improved. Despite the

permissiveness of the definition the majority of species show a-symmetrical cooperative

directionality with a single giver (Supplementary Table S10). We explored the

symmetrical interaction on different growth media. Growth media were determined by

setting thresholds on the feasible solution which required achieving at least X% of the

corresponding growth in COMPM. The thresholds were set on both the biomass

production of the multi-species system (as in Supplementary Table S10) and on the

contained organisms (i.e. requiring that each compartment/organism will have a biomass

production rate higher than a threshold, in comparison to its growth in COMPM). This

indeed raised the number of symmetrical events (Supplementary Table S11), though it

reduced the ecological signal, where similar fractions of cooperative events are observed

for the group of niche-associated and non-associated pairs, testifying against the

ecological relevance of enhancing the propensity of symmetrical solutions via such

means.

Supplementary Note 6: Experimental and computational growth analyses of Listeria

innocua and Agrobacterium tumefaciens across pre-designed media

In order to explore the predictive power of our co-growth simulations in changing

environments, we first identified the limiting factors of Listeria innocua and

29

Agrobacterium tumefaciens at their simulated IMM (i.e. where the metabolites are

consumed at the maximal threshold determined, Vi, Min_FVA =-50, Supplementary

Methods). As can be expected from the neutral interactions between the two organisms

(predicted and observed, Supplementary Methods), most of their limiting factors at the

simulated-IMM do not overlap (Supplementary Table S12). The predicted limiting

factors of L. innocua are glutamine, glucose and cysteine. The predicted limiting factors

of A. tumefaciens are isoleucine, glutamine and histidine. Simulations were then

conducted while decreasing the level of these metabolites (that is increasing Vi, Min_FVA) at

different thresholds until their full removal (Vi, Min_FVA=0). For the four amino acids

studied, decreasing the corresponding fluxes slowed down the growth rate of the relevant

species (cysteine and glutamine for L. innocua and histidne glutamine and isoleucine for

A. tumefaciens), but had only a minor effect pattern of co growth (Supplementary Table

S12). The predictions for the growth and co-growth patterns following the removal of

cysteine and histidine, predicted to have the most significant effect on growth

(Supplementary Table S12), were further tested experimentally. Laboratory observations

indicate that the effect of metabolites removal on both growth and co-growth patterns can

be fully predicted: the growth of A. tumefaciens is affected by the removal of histidine

but not cysteine and the growth of L. innocua is affected by the removal of cysteine but

not histidine (Supplementary Table S13). In both cases, co-growth pattern remains

relatively similar to the pattern observed in the original media (Supplementary Table

S13).

The computational predictions indicate that decreasing the level of glucose is

likely to affect both individual and co-growth patterns (Supplementary Table S12). At

30

individual growth, decreasing the level of glucose is likely to affect only L. innocua,

slowing down its growth, but at co-growth it is predicted to increase the inter-species

competition (possibly due to the resource overlap induced by the shortage in glucose).

The full removal of glucose is predicted to prevent the growth of L. innocua (again, with

no effect on A. tumefaciens), where co-growth is predicted to induce a modest level of

cooperation (Supplementary Table S13). Reassuringly, experimental tests support the

computational predictions where the removal of glucose from the media prevents the

growth of L. innocua but has no effect on A. tumefaciens. As predicted, decreasing the

level of glucose increases the competition at co-growth. However, at full removal we do

not observe the predicted mild cooperation.

Overall, in most growth experiments (7/8) predictions and observations correlate

(Supplementary Table S13). When looking at co-growth predictions, the most significant

growth ratio change occurs at the partial and full removal of glucose. In agreement with

the predictions, the partial elimination of glucose induced the most drastic elevation in

growth rate ratio, where, as predicted, a weaker effect is observed for the removal of

amino acids. With the exception of a single experiment experimental measure (co-growth

pattern following full removal of glucose), we observe an overall agreement between

predictions and observations. Hence, overall, this set of experiments supports the ability

of the metabolic models to predict the growth pattern of species at varying environments.

31

Supplementary Methods

Computing the Maximal Biomass Production Rate (MBR) of species

Constraint-Based Modeling (CBM) was used in order to simulate co-growth in two-

species systems, where species are represented by genome-scale metabolic models.

Briefly, in these models, a stoichiometric matrix (S) is used to encode the information

about the topology and mass balance in a metabolic network, including the complete set

of enzymatic and transport reactions in the system and its biomass reaction. Reactions are

inferred from genome annotations and specialized prediction tools. Given a metabolic

model, Constraint-Based Modeling (CBM) provides a solution space in terms of

predicted fluxes that is consistent with the constraints set up by the model. Flux balance

analysis (FBA)42

is a CBM method that further constrains the solution space by solving a

linear problem of maximizing or minimizing a biomass production rate objective

function43-44

. The biomass production rate describes the rate of production of a set of

metabolites required for cellular growth, where a higher biomass flux corresponds with a

faster growth rate of the organism45.

Here, 160 metabolic models were retrieved from The Seed's metabolic models

section (http://seed-viewer.theseed.org/seedviewer.cgi?page=ModelViewer)14

. The

models are automatically constructed by a pipeline that starts with a complete genome

sequence as an input and integrates numerous technologies such as genome annotation,

reaction network annotation and assembly, determination of reaction reversibility, and

model optimization to fit experimental data. For each species we calculate its maximal

biomass production rate (MBR) by assuming that all exchange reactions can be

32

potentially fully active (which is equivalent to assuming a rich media). The upper and

lower bounds of exchange and non exchange reactions are conventionally set as follows:

For irreversible reactions:

Exchange reactions:

0 ≤ Vi,ex ≤ Vi, Max_ex (Vi, Max_ex = 1000) (S1)

Non exchange reactions:

0 ≤ Vi ≤ Vi, Max ( Vi, Max = 1000) (S2)

For reversible reactions:

Exchange reactions:

Vi, Min_ex ≤ Vi,ex ≤ Vi, Max_ex (Vi, Min_ex = -50 Vi, Max_ex = 1000) (S3)

Non-exchange reactions:

Vi, Min ≤ Vi ≤ Vi, Max {Vi, Min = -1000 Vi, Max = 1000} (S4)

Simulations were run using the "ILOG CPLEX" solver using the "Condor" platform46

.

Following the filtering out of different strains of the same species and models that did not

have a biomass reaction defined or that their biomass reaction could not be activated, we

were left with a final set of 118 models (see Supplementary Table S2). In addition to the

118 bacterial models, a metabolic model for H. walsbyi (archaea) was constructed by

using the SEED tool. This model was only used for growth simulations with Salinibacter

ruber.

33

Generation of a multi-species system metabolic model

Our approach for generating multi-species models follows the definition employed by13

.

Briefly, we converted the model of each organism into a compartment in a multi-species

system. For two species A and B this system consists of: [CA]=cytoplasm compartment of

species A; [CB]=cytoplasm compartment of species B; and [EAB]=Extra-cellular

compartment of species A and B. CA and CB include all non-exchange and transport

reactions of the corresponding species . EAB includes the union of the exchange reactions

of A and B. The objective function of the multi-species system was defined as the sum of

the biomass reactions of the member organisms. This method is used in our setup for

simulating pairwise growth but is applicable for any number of organisms. Applying the

multi-species system analysis to all possible pairwise combinations of our 118 metabolic

models, we examined 6903 unique pairs whose growth can be simulated under a range of

environments.

Computing a Competition inducing Medium (COMPM) for single species and multi-

species systems

For a single species model A, COMPM is defined as the ranges of fluxes of the exchange

reactions {VCOMPM,A} that supports its maximal biomass rate (MBR), when all

exchange metabolites are provided at the minimal required amount. The latter is found

by Flux Variability Analysis (FVA)42

, where Vi, Min_FVA then denotes the lower limit

(maximal flux of metabolites into a compartment) of a given reaction. Following our

34

definitions, an increase in (the negative) Vi, Min_FVA will effectively limit the flux of

metabolites into the compartment and prevent species A from reaching its MBR. Notably,

for the large majority of exchange reactions (70%-80%) Vi, Min_FVA = Vi,max FVA. To

relate COMPM environments to real ecological conditions we verified that species

inhabiting similar environments tend to have similar metabolic profiles, as previously

demonstrated in30

. As documented in many laboratory experiments, typical limiting

factors in COMPM environments include oxygen, glucose and nitrogen sources

(Supplementary Note 4). Finally, computational simulation providing predictions for the

effect of removal of chosen metabolites on species growth were experimentally tested,

supporting the ability of the models to identify growth limiting factors (Supplementary

Note 6). The full description of VCOMPM, A is provided at Supplementary Data 8.

A similar computation is done for species B and then, in the multi-species system of A

and B, we define:

{VCOMPM, AB} = {VCOMPM, A} U {VCOMPM, B} (S5)

Vi, Min_FVA,AB = Min(Vi, Min_FVA,A , Vi, Min_FVA,B)) (S6)

so that the lower bound of each reaction is set according to the lower FVA limit,

considering the species involved. By definition, at individual growth, COMPMAB allows

A and B to reach their MBR. However, at co-growth, any resource overlap will prevent

species A and B to simultaneously reach their MBR, and reveal potential sources of

competition. The full description of VCOMP, AB is provided at Supplementary Data 9.

35

Computing a Cooperation-inducing Medium (COOPM)

A cooperation-inducing medium (COOPM) for a multi-species system is defined here as

a set of metabolites that allows the system to obtain a positive growth rate (above a

certain predetermined threshold, which may yet be far from optimal), and such that the

removal of any metabolite from the set would force the system to have no such solution.

A feasible solution in this context is defined as one achieving at least 10% of the joint

MBR obtained when grown on a rich medium (COMPM). The use of other MBR

thresholds is examined at Supplementary Note 5. COOPM is calculated using mixed

integer linear programming as in47-48

, as described below:

As a first step we start with {VCOMPM, AB}, a set of exchange reactions flux ranges as

defined above. We then solve a minimization problem which uses, in addition to the

usual FBA constraints: (i) a constraint on minimal growth rates, VBM-COOPM ≥ 0.1 x VBM-

COMPM where VBM-COOPM and VBM-COMPM are the Biomass Production rates on COOPM

and COMPM respectively (ii) a constraint expressing whether or not an exchange

metabolite i is consumed: Vi, COOPM, AB + Vi,min θi ≥ Vi,min, where Vi, COOPM, AB is the flux

running through the exchange reaction i, and Vi, COOPM, AB ≤ 0 when the metabolite i is

consumed (negative flux). Here, the binary variable θi attains a value of 1 if metabolite i

is not consumed (Vi, COOPM, AB ≥ 0) by any of the organisms, and 0 otherwise.

Identifying a minimal set of metabolites in a medium then amounts to maximizing the

sum of the θi variables over all metabolites in {Vi, COMPM, AB }. Overall, the optimization

problem can be expressed as follows:

36

, ,

, min , max

, ,

, , , min , min

, ,

max

:

0

/10

{0,1}

n

i

i COMPM AB

j j j

BM COOPM BM COMPM

i COOPM AB i i i

i COOPM AB

j

i V

Subject to

SV

V V V

V V

V V V

i V

v V

θ

θ

θ

=

=

≤ ≤

≥

+ ≥

∈

∈

∈

∑

(S7)

The bounds on the active exchange reactions are set to their COMPM value. The full

description of VMM, AB is provided at Supplementary Data 10.

Experimental and computational co-growth analysis

Co-growth experiments were conducted between all co-growth combinations

formed between five species, all non-pathogenic and capable of growing in IMM. The

species and their seed models are the following: Listeria innocua Clip11262

(Core272626_1), Agrobacterium tumefaciens str. C58 (Core176299_3), Escherichia coli

K12 (Core83333_1), Pseudomonas aeruginosa PAO1 (Core208964_1), Bacillus subtilis

str. 168 (Opt224308_1).

Growth experiments for each individual and pairwise combinations were

conducted in IMM defined medium49, in 96-well plates at 30°C with continuous shaking,

37

using the Biotek ELX808IU-PC microplate reader. Optical density was measured every

15 minutes at a wavelength of 595nm.

A simulated medium was designed to match the defined medium with minimal

modifications allowing co-growth (Supplementary Table S4). For L. innocua (strain

Clip1126) and A. tumefaciens (strain C58), we both predict and observe a neutral

interaction in the given media. That is, the Sum of Individual Growth (SIG)

approximately equals the total Co-Growth in a multi-species system (CG). To simulate a

negative shift (SIG/CG > SIG/CGneutral_medium), co-growth simulations were conducted

when adding all one- and two-compound combinations of exchange metabolites to the

simulated IMM (considering all exchange metabolites of the given species). To simulate

a positive shift (SIG/CG < SIG/CGneutral_medium), co-growth simulations were conducted,

subtracting all one- or two-pertaining compound combinations from the simulated IMM.

Co-growth simulations were conducted across all subtraction/addition combinations;

subsequently we chose the media inducing the most prominent shifts. A table describing

co-growth patterns across all reductive combinations is provided at Supplementary Data

11. Based on the selected predictions, the experimental media were modified by adding

thymidine and xylose (for a negative shift) and by the subtraction of thiamine and glucose

(positive shift). The growth experiments for the additional 9 bacterial pairs are shown in

Supplementary Note 1. Growth experiments in additional selected shifted media are

described at Supplementary Note 6.

Finding close cooperative loops in real and random networks of give-take

interactions and in real and randomly drawn communities

38

Starting from the network of 'give-take' interactions we derived two sub-networks: the

ecologically-associated sub-network including the edges between ecologically-associated

species, and the non ecologically-associated sub-network including the edges between the

non associated species. The original network is provided at Supplementary Data 1,

indicating the type of ecological association corresponding to each edge. The original

network and the sub-networks of ecologically associated and non-associated species are

composed of 80, 66, and 80 nodes and 3160, 648 and 2512 edges, respectively. For each

of the two sub-networks, the number of loops was compared to the number found in 1000

random networks. Random networks were generated by shuffling edges, retaining node

number and edge degree. Notably, in the network describing interactions between niche-

associated pairs the number of loops is significantly higher than random (t test < 0.001),

unlike in the network describing non ecological-associations. Real communities were

derived from the ecological distribution data where a community represents the set of

species detected in a given sample (as listed in Supplementary Data 5). The rate of

occurrence of close cooperative cycles was recorded (1) across all true samples (194

appearances) (2) and across 1000 data sets generated through random shuffling of the

original samples data while maintaining the same sample size distribution and the same

rank of species' appearances as in the original data.

39

Supplementary References

39 Madan Babu, M., Teichmann, S. A. & Aravind, L. Evolutionary dynamics of

prokaryotic transcriptional regulatory networks. J Mol Biol 358, 614-633, (2006).

40 Parter, M., Kashtan, N. & Alon, U. Environmental variability and modularity of

bacterial metabolic networks. BMC Evol Biol 7, 169, (2007).

41 Vieira-Silva, S. & Rocha, E. P. The systemic imprint of growth and its uses in

ecological (meta)genomics. PLoS Genet 6, e1000808, (2010).

42 Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in

constraint-based genome-scale metabolic models. Metab Eng 5, 264-276, (2003).

43 Edwards, J. S., Ramakrishna, R. & Palsson, B. O. Characterizing the metabolic

phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 77, 27-36,

(2002).

44 Varma, A., Boesch, B. W. & Palsson, B. O. Biochemical production capabilities

of Escherichia coli. Biotechnol Bioeng 42, 59-73, (1993).

45 Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively

predict growth and metabolic by-product secretion in wild-type Escherichia coli

W3110. Appl Environ Microbiol 60, 3724-3731 (1994).

46 Livny, D. T. a. T. T. a. M. Distributed computing in practice: the Condor

experience. Concurrency - Practice and Experience 17, 323-356 (2005).

47 Burgard, A. P., Vaidyaraman, S. & Maranas, C. D. Minimal reaction sets for

Escherichia coli metabolism under different growth requirements and uptake

environments. Biotechnol Prog 17, 791-797, (2001).

48 Suthers, P. F. et al. A genome-scale metabolic reconstruction of Mycoplasma

genitalium, iPS189. PLoS Comput Biol 5, e1000285, (2009).

49 Phan-Thanh, L. & Gormon, T. A chemically defined minimal medium for the

optimal culture of Listeria. Int J Food Microbiol 35, 91-95, (1997).

50 Maglott, D., Ostell, J., Pruitt, K. D. & Tatusova, T. Entrez Gene: gene-centered

information at NCBI. Nucleic Acids Res 33, D54-58, (2005).